System Identification Toolbox

System Identification Toolbox
System Identification
Toolbox
For Use with MATLAB
®
Lennart Ljung
Computation
Visualization
Programming
User’s Guide
Version 5
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System Identification Toolbox User’s Guide
 COPYRIGHT 1988 - 2001 by The MathWorks, Inc.
The software described in this document is furnished under a license agreement. The software may be used
or copied only under the terms of the license agreement. No part of this manual may be photocopied or reproduced in any form without prior written consent from The MathWorks, Inc.
FEDERAL ACQUISITION: This provision applies to all acquisitions of the Program and Documentation by
or for the federal government of the United States. By accepting delivery of the Program, the government
hereby agrees that this software qualifies as "commercial" computer software within the meaning of FAR
Part 12.212, DFARS Part 227.7202-1, DFARS Part 227.7202-3, DFARS Part 252.227-7013, and DFARS Part
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to the government’s use and disclosure of the Program and Documentation, and shall supersede any
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is inconsistent in any respect with federal procurement law, the government agrees to return the Program
and Documentation, unused, to MathWorks.
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Other product or brand names are trademarks or registered trademarks of their respective holders.
Printing History: April 1988
July 1991
May 1995
November 2000
April 2001
First printing
Second printing
Third printing
Fourth printing for Version 5.0 (Release 12)
Fifth printing
Contents
Preface
What Is the System Identification Toolbox? . . . . . . . . . . . . . . . x
Using This Guide . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi
Typographical Conventions . . . . . . . . . . . . . . . . . . . . . . . . . . . . xii
Related Products . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii
About the Author . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xv
The System Identification Problem
1
Basic Questions About System Identification . . . . . . . . . . . . 1-2
Common Terms Used in System Identification . . . . . . . . . . 1-4
Basic Information About Dynamic Models . . . . . . . . . . . . . . 1-6
The Signals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-6
The Basic Dynamic Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-7
Variants of Model Descriptions . . . . . . . . . . . . . . . . . . . . . . . . . 1-7
How to Interpret the Noise Source . . . . . . . . . . . . . . . . . . . . . . . 1-8
Terms to Characterize the Model Properties . . . . . . . . . . . . . . 1-10
The Basic Steps of System Identification . . . . . . . . . . . . . . . 1-12
A Startup Identification Procedure . . . . . . . . . . . . . . . . . . . . 1-14
Step 1: Looking at the Data . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-14
Step 2: Getting a Feel for the Difficulties . . . . . . . . . . . . . . . . 1-14
i
Step 3: Examining the Difficulties . . . . . . . . . . . . . . . . . . . . . . 1-15
Step 4: Fine Tuning Orders and Disturbance Structures . . . . 1-16
Multivariable Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-18
Reading More About System Identification . . . . . . . . . . . . . 1-21
The Graphical User Interface
2
The Big Picture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
The Model and Data Boards . . . . . . . . . . . . . . . . . . . . . . . . . . . .
The Working Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
The Views . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
The Validation Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
The Work Flow . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Management Aspects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Workspace Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Help Texts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2-2
2-2
2-3
2-3
2-4
2-4
2-4
2-5
2-6
Handling Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-7
Getting Input-Output Data into the GUI . . . . . . . . . . . . . . . . . . 2-8
Taking a Look at the Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-10
Preprocessing Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-11
Checklist for Data Handling . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-13
Simulating Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-13
Estimating Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Direct Estimation of the Impulse Response . . . . . . . . . . . . . . .
Direct Estimation of the Frequency Response . . . . . . . . . . . . .
Estimation of Parametric Models . . . . . . . . . . . . . . . . . . . . . . .
ARX Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
ARMAX, Output-Error and Box-Jenkins Models . . . . . . . . . . .
State-Space Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
User Defined Model Structures . . . . . . . . . . . . . . . . . . . . . . . . .
2-15
2-15
2-16
2-17
2-21
2-23
2-25
2-26
Examining Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-28
Views and Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-28
ii
Contents
The Plot Windows . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Frequency Response and Disturbance Spectra . . . . . . . . . . . .
Transient Response . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Poles and Zeros . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Compare Measured and Model Output . . . . . . . . . . . . . . . . . . .
Residual Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Text Information . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
LTI Viewer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Further Analysis in the MATLAB Workspace . . . . . . . . . . . . .
2-29
2-30
2-30
2-31
2-31
2-32
2-33
2-33
2-34
Some Further GUI Topics . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Troubleshooting in Plots . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Layout Questions and idprefs.mat . . . . . . . . . . . . . . . . . . . . . .
Customized Plots . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
What Cannot be Done Using the GUI . . . . . . . . . . . . . . . . . . .
2-35
2-36
2-36
2-37
2-37
Tutorial
3
Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-2
The Toolbox Commands . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-3
An Introductory Example to Command Mode . . . . . . . . . . . . 3-5
The System Identification Problem . . . . . . . . . . . . . . . . . . . . . 3-9
Polynomial Representation of Transfer Functions . . . . . . . . . 3-11
State-Space Representation of Transfer Functions . . . . . . . . . 3-13
Continuous-Time State-Space Models . . . . . . . . . . . . . . . . . . . 3-14
Estimating Impulse Responses . . . . . . . . . . . . . . . . . . . . . . . . . 3-15
Estimating Spectra and Frequency Functions . . . . . . . . . . . . . 3-15
Estimating Parametric Models . . . . . . . . . . . . . . . . . . . . . . . . . 3-16
Subspace Methods for Estimating State-Space Models . . . . . . 3-17
Data Representation and
Nonparametric Model Estimation . . . . . . . . . . . . . . . . . . . . . 3-18
Correlation Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-19
iii
Spectral Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-19
More on the Data Representation in iddata . . . . . . . . . . . . . . . 3-21
iv
Contents
Parametric Model Estimation . . . . . . . . . . . . . . . . . . . . . . . . .
ARX Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AR Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
General Polynomial Black-Box Models . . . . . . . . . . . . . . . . . . .
State-Space Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Optional Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3-25
3-26
3-26
3-27
3-28
3-30
Defining Model Structures . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Polynomial Black-Box Models: The idpoly Model . . . . . . . . . .
Multivariable ARX Models: The idarx Model . . . . . . . . . . . . . .
Black-Box State-Space Models: the idss Model . . . . . . . . . . . .
Structured State-Space Models with Free Parameters:
the idss Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
State-Space Models with Coupled Parameters:
the idgrey Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
State-Space Structures: Initial Values and
Numerical Derivatives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3-35
3-36
3-37
3-39
Examining Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Parametric Models: idmodel and its children . . . . . . . . . . . . . .
Frequency Function Format: the idfrd model . . . . . . . . . . . . .
Graphs of Model Properties . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Transformations to Other Model Representations . . . . . . . . .
Discrete and Continuous Time Models . . . . . . . . . . . . . . . . . . .
3-49
3-49
3-55
3-56
3-59
3-60
Model Structure Selection and Validation . . . . . . . . . . . . . .
Comparing Different Structures . . . . . . . . . . . . . . . . . . . . . . . .
Impulse Response to Determine Delays . . . . . . . . . . . . . . . . . .
Checking Pole-Zero Cancellations . . . . . . . . . . . . . . . . . . . . . . .
Residual Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Model Error Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Noise-Free Simulations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Assessing the Model Uncertainty . . . . . . . . . . . . . . . . . . . . . . .
Comparing Different Models . . . . . . . . . . . . . . . . . . . . . . . . . . .
Selecting Model Structures for Multivariable Systems . . . . . .
3-63
3-63
3-66
3-66
3-66
3-67
3-68
3-68
3-70
3-70
3-42
3-44
3-47
Dealing with Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Outliers and Bad Data; Multi-Experiment Data . . . . . . . . . . .
Missing Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Filtering Data: Focus . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Feedback in Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Delays . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3-74
3-74
3-75
3-75
3-76
3-77
Recursive Parameter Estimation . . . . . . . . . . . . . . . . . . . . . .
The Basic Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Choosing an Adaptation Mechanism and Gain . . . . . . . . . . . .
Available Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Segmentation of Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3-78
3-78
3-79
3-81
3-83
Some Special Topics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Periodic Inputs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Connections Between the Control System Toolbox and the
System Identification Toolbox . . . . . . . . . . . . . . . . . . . . . . . . . .
Memory - Speed Trade-Offs . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Local Minima . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Initial Parameter Values . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Initial State . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
The Estimated Parameter Covariance Matrix . . . . . . . . . . . . .
No Covariance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
nk and InputDelay . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Linear Regression Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Spectrum Normalization and the Sampling Interval . . . . . . .
Interpretation of the Loss Function . . . . . . . . . . . . . . . . . . . . .
Enumeration of Estimated Parameters . . . . . . . . . . . . . . . . . .
Complex-Valued Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Strange Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3-85
3-87
3-87
3-89
3-90
3-90
3-91
3-92
3-92
3-93
3-94
3-94
3-97
3-98
3-98
3-99
Command Reference
4
aic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-9
Algorithm Properties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-10
ar . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-16
armax . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-19
v
arx . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-22
arxdata . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-24
arxstruc . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-25
bj . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-27
bode . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-29
compare . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-32
covf . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-34
cra . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-35
c2d . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-37
detrend . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-38
d2c . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-39
EstimationInfo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-41
etfe . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-43
ffplot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-45
freqresp . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-46
fpe . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-48
get . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-49
getexp . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-50
idarx . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-51
iddata . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-54
ident . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-60
idfilt . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-61
idfrd . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-63
idgrey . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-69
idinput . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-74
idmodel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-77
idmodred . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-85
idpoly . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-86
idss . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-90
impulse . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-96
init . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-99
ivar . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-100
ivstruc . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-101
ivx . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-103
iv4 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-104
LTI Commands . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-105
merge (iddata) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-106
merge (idmodel) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-108
midprefs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-109
misdata . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-110
vi
Contents
nkshift . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
noisecnv . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
nuderst . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
nyquist . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
n4sid . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
oe . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
pe . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
pem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
plot (iddata) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
plot (idmodepolydata) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
predict . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
present . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
pzmap . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
rarmax . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
rarx . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
rbj . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
resample . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
resid . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
roe . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
rpem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
rplr . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
segment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
selstruc . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
set . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
setpname . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
sim . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
simsd . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
size . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
spa . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
ss, tf, zpk, frd . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
ssdata . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
step . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
struc . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
timestamp . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
tfdata . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
view . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
zpkdata . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4-111
4-112
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4-122
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vii
viii Contents
Preface
What Is the System Identification Toolbox? . . . . . . . x
Using This Guide . . . . . . . . . . . . . . . . . . . xi
Typographical Conventions . . . . . . . . . . . . . .xii
Related Products . . . . . . . . . . . . . . . . . . xiii
About the Author . . . . . . . . . . . . . . . . . . . xv
Preface
What Is the System Identification Toolbox?
The System Identification Toolbox is for building accurate, simplified models
of complex systems from noisy time-series data.
It provides tools for creating mathematical models of dynamic systems based
on observed input/output data. The toolbox features a flexible graphical user
interface that aids in the organization of data and models. The identification
techniques provided with this toolbox are useful for applications ranging from
control system design and signal processing to time-series analysis and
vibration analysis.
x
Using This Guide
Using This Guide
System Identification is about building mathematical models of dynamic
systems based on measured data. Some knowledge about such models is
therefore necessary for successful use of the toolbox.The topic is treated in
several places in Chapter 3, “Tutorial” and there is a wide range of textbooks
available for introductory and in-depth studies. For basic use of the toolbox, it
is sufficient to have quite superficial insights about dynamic models. For
review of basic knowledge, see “How do I get started?” on page 1-3.
If you are a beginner, browse through Chapter 2, “The Graphical User
Interface” and try out a couple of the data sets that come with the toolbox. Use
the graphical user interface (GUI) and check out the built-in help functions to
understand what you are doing.
xi
Preface
Typographical Conventions
This manual uses some or all of these conventions.
Item
Convention Used
Example
Ellipsis
(...) ellipsis denotes all of the
syntaxes that came before.
[c,ia,ib] = union(...)
Example code
Monospace font
To assign the value 5 to A,
enter
A = 5
Function names/syntax
Monospace font
The cos function finds the
cosine of each array element.
Syntax line example is
MLGetVar ML_var_name
Keys
Boldface with an initial capital
letter
Press the Return key.
Literal strings (in syntax
descriptions in reference
chapters)
Monospace bold for literals
f = freqspace(n,'whole')
Mathematical
expressions
Italics for variables
This vector represents the
polynomial
MATLAB output
Standard text font for functions,
operators, and constants
Monospace font
p = x2 + 2x + 3
MATLAB responds with
A =
5
xii
Menu titles, menu items,
dialog boxes, and controls
Boldface with an initial capital
letter
Choose the File menu.
New terms
Italics
An array is an ordered
collection of information.
String variables (from a
finite list)
Monospace italics
sysc = d2c(sysd,'method')
Related Products
Related Products
The MathWorks provides several products that are especially relevant to the
kinds of tasks you can perform with the System Identification Toolbox. In
particular, the Systems Identification Toolbox requires these products:
• MATLAB®
For more information about any of these products, see either:
• The online documentation for that product, if it is installed or if you are
reading the documentation from the CD
• The MathWorks Web site, at http://www.mathworks.com; see the “products”
section
Note The products listed below complement the functionality of the System
Identification toolbox.
Product
Description
Simulink®
Interactive, graphical environment for modeling, simulating, and
prototyping dynamic systems
Control System Toolbox
Tool for modeling, analyzing, and designing control systems using
classical and modern techniques
Data Acquisition Toolbox
MATLAB functions for direct access to live, measured data from
MATLAB
Financial Time Series
Toolbox
Tool for analyzing time series data in the financial markets
Financial Toolbox
MATLAB functions for quantitative financial modeling and analytic
prototyping
Fuzzy Logic Toolbox
Tool to help master fuzzy logic techniques and their application to
practical control problems
xiii
Preface
xiv
Product
Description
µ-Analysis and Synthesis
Toolbox
Computational algorithms for the structured singular value, µ,
applicable to robustness and performance analysis for systems with
modeling and parameter uncertainties
Neural Network Toolbox
Comprehensive environment for neural network research, design,
and simulation within MATLAB
Optimization Toolbox
Tool for general and large-scale optimization of nonlinear problems,
as well as for linear programming, quadratic programming,
nonlinear least squares, and solving nonlinear equations
Robust Control Toolbox
Tools for modeling, analysis, and design of “robust” multivariable
feedback control systems using H∞ techniques
Signal Processing
Toolbox
Tool for algorithm development, signal and linear system analysis,
and time-series data modeling
Statistics Toolbox
Tool for analyzing historical data, modeling systems, developing
statistical algorithms, and learning and teaching statistics
About the Author
About the Author
Lennart Ljung received his PhD in Automatic Control from Lund Institute of
Technology in 1974. Since 1976 he is Professor of the chair of Automatic
Control in Linkoping, Sweden, and is currently Director of the Center for the
“Information Systems for Industrial Control and Supervision” (ISIS). He has
held visiting positions at Stanford and MIT and has written several books on
System Identification and Estimation. He is an IEEE Fellow, an IFAC Advisor,
a member of the Royal Swedish Academy of Sciences (KVA) and of the Royal
Swedish Academy of Engineering Sciences (IVA), and has received honorary
doctorates from the Baltic State Technical University in St Petersburg, and
from Uppsala University.
xv
Preface
xvi
1
The System Identification
Problem
Basic Questions About System Identification . . . . . 1-2
Common Terms Used in System Identification
Basic Information About Dynamic Models
The Signals . . . . . . . . . . . . . . .
The Basic Dynamic Model . . . . . . . . .
Variants of Model Descriptions . . . . . . .
How to Interpret the Noise Source . . . . . .
Terms to Characterize the Model Properties .
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. 1-6
. 1-6
. 1-7
. 1-7
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. 1-10
The Basic Steps of System Identification . . . . . . . 1-12
A Startup Identification Procedure . . . . . . .
Step 1: Looking at the Data . . . . . . . . . . . .
Step 2: Getting a Feel for the Difficulties . . . . . . .
Step 3: Examining the Difficulties . . . . . . . . . .
Step 4: Fine Tuning Orders and Disturbance Structures .
Multivariable Systems . . . . . . . . . . . . . .
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. 1-14
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. 1-18
Reading More About System Identification . . . . . . 1-21
1
The System Identification Problem
Basic Questions About System Identification
What is System Identification?
System Identification allows you to build mathematical models of a dynamic
system based on measured data.
How is that done?
Essentially by adjusting parameters within a given model until its output
coincides as well as possible with the measured output.
How do you know if the model is any good?
A good test is to take a close look at the model’s output compared to the
measured one on a data set that wasn’t used for the fit (“Validation Data”).
Can the quality of the model be tested in other ways?
It is also valuable to look at what the model couldn’t reproduce in the data (“the
residuals”). This should not be correlated with other available information,
such as the system's input.
What models are most common?
The techniques apply to very general models. Most common models are
difference equations descriptions, such as ARX and ARMAX models, as well as
all types of linear state-space models.
Do you have to assume a model of a particular type?
For parametric models, you have to specify the structure. This could be as easy
as just selecting a single integer, the model order, or may involve several
choices.If you just assume that the system is linear, you can directly estimate
its impulse or step response using Correlation Analysis or its frequency
response using Spectral Analysis. This allows useful comparisons with other
estimated models.
What does the System Identification Toolbox contain?
It contains all the common techniques to adjust parameters in all kinds of
linear models. It also allows you to examine the models’ properties, and to
check if they are any good, as well as to preprocess and polish the measured
data.
1-2
Basic Questions About System Identification
Isn’t it a big limitation to work only with linear models?
No, actually not. Many common model nonlinearities are such that the
measured data should be nonlinearly transformed (like squaring a voltage
input if you think that it’s the power that is the stimuli). Use physical insight
about the system you are modeling and try out such transformations on models
that are linear in the new variables, and you will cover a lot!
How do I get started?
If you are a beginner, browse through Chapter 2, “The Graphical User
Interface.” Then try out a couple of the data sets that come with the toolbox.
Use the graphical user interface (GUI) and check out the built-in help functions
to understand what you are doing.
Is this really all there is to System Identification?
Actually, there is a huge amount written on the subject. Experience with real
data is the driving force to understand more. It is important to remember that
any estimated model, no matter how good it looks on your screen, has only
picked up a simple reflection of reality. Surprisingly often, however, this is
sufficient for rational decision making.
1-3
1
The System Identification Problem
Common Terms Used in System Identification
This section defines some of the terms that are frequently used in System
Identification:
• Estimation Data is the data set that is used to fit a model to data. In the GUI
this is the same as the Working Data.
• Validation Data is the data set that is used for model validation purposes.
This includes simulating the model for these data and computing the
residuals from the model when applied to these data.
• Model Views are various ways of inspecting the properties of a model. They
include looking at zeros and poles, transient and frequency response, and
similar things.
• Data Views are various ways of inspecting properties of data sets. A most
common and useful thing is just to plot the data and scrutinize it.
So-called outliers could be detected then. These are unreliable
measurements, perhaps arising from failures in the measurement
equipment. The frequency contents of the data signals, in terms of
periodograms or spectral estimates, is also most revealing to study.
• Model Sets or Model Structures are families of models with adjustable
parameters. Parameter Estimation amounts to finding the “best” values of
these parameters. The System Identification problem amounts to finding
both a good model structure and good numerical values of its parameters.
• Parametric Identification Methods are techniques to estimate parameters
in given model structures. Basically it is a matter of finding (by numerical
search) those numerical values of the parameters that give the best
agreement between the model’s (simulated or predicted) output and the
measured one.
• Nonparametric Identification Methods are techniques to estimate model
behavior without necessarily using a given parametrized model set.
Typical nonparametric methods include Correlation analysis, which
estimates a system’s impulse response, and Spectral analysis, which
estimates a system’s frequency response.
1-4
Common Terms Used in System Identification
• Model Validation is the process of gaining confidence in a model.
Essentially this is achieved by “twisting and turning” the model to scrutinize
all aspects of it. Of particular importance is the model’s ability to reproduce
the behavior of the Validation Data sets. Thus it is important to inspect the
properties of the residuals from the model when applied to the Validation
Data.
1-5
1
The System Identification Problem
Basic Information About Dynamic Models
System Identification is about building Dynamic Models. Some knowledge
about such models is therefore necessary for successful use of the toolbox. The
topic is treated in several places in Chapter 3, “Tutorial.” Also, there is a wide
range of textbooks available for introductory and in-depth studies. For basic
use of the toolbox, it is sufficient to have quite superficial insights about
dynamic models. This section describes such a basic level of knowledge.
The Signals
Models describe relationships between measured signals. It is convenient to
distinguish between input signals and output signals. The outputs are then
partly determined by the inputs. Think for example of an airplane where the
inputs would be the different control surfaces, ailerons, elevators, and the like,
while the outputs would be the airplane’s orientation and position. In most
cases, the outputs are also affected by more signals than the measured inputs.
In the airplane example it would be wind gusts and turbulence effects. Such
‘‘unmeasured inputs’’ will be called disturbance signals or noise. If we denote
inputs, outputs, and disturbances by u, y, and e, respectively, the relationship
can be depicted in the following figure.
e
u
y
Figure 1-1: Input Signals u, Output Signals y, and Disturbances e
All these signals are functions of time, and the value of the input at time t will
be denoted by u(t). Often, in the identification context, only discrete-time points
are considered, since the measurement equipment typically records the signals
just at discrete-time instants, often equally spread in time with a sampling
interval of T time units. The modeling problem is then to describe how the
three signals relate to each other.
1-6
Basic Information About Dynamic Models
The Basic Dynamic Model
The basic relationship is the linear difference equation. An example of such
an equation is the following one.
y ( t ) – 1.5y ( t – T ) + 0.7y ( t – 2T ) = 0.9u ( t – 2T ) + 0.5u ( t – 3T )
( ARX )
Such a relationship tells us, for example, how to compute the output y(t) if the
input is known and the disturbance can be ignored:
y ( t ) = 1.5y ( t – T ) – 0.7y ( t – 2T ) + 0.9u ( t – 2T ) + 0.5u ( t – 3T )
The output at time t is thus computed as a linear combination of past outputs
and past inputs. It follows, for example, that the output at time t depends on
the input signal at many previous time instants. This is what the word
dynamic refers to. The identification problem is then to use measurements of
u and y to figure out:
• The coefficients in this equation (i.e., -1.5, 0.7, etc.).
• How many delayed outputs to use in the description (two in the example:
y (t-T) and y (t-2T) ) .
• The time delay in the system is (2T in the example: you see from the second
equation that it takes 2T time units before a change in u will affect y).
• How many delayed inputs to use (two in the example: u(t-2T) and u(t-3T)).
The number of delayed inputs and outputs are usually referred to as the
model order(s).
Variants of Model Descriptions
The model given above is called an ARX model. There are a handful of variants
of this model known as Output-Error (OE) models, ARMAX models, FIR
models, and Box-Jenkins (BJ) models. These are described later on in the
manual. At a basic level it is sufficient to think of them as variants of the ARX
model allowing also a characterization of the properties of the disturbances e.
Linear state-space models are also easy to work with. The essential structure
variable is just a scalar: the model order. This gives just one knob to turn when
searching for a suitable model description. See below.
General linear models can be described symbolically by
y=G u+He
1-7
1
The System Identification Problem
which says that the measured output y(t) is a sum of one contribution that
comes from the measured input u(t) and one contribution that comes from the
noise He. The symbol G then denotes the dynamic properties of the system,
that is, how the output is formed from the input. For linear systems it is called
the transfer function from input to output. The symbol H refers to the noise
properties, and is called the disturbance model. It describes how the
disturbances at the output are formed from some standardized noise source
e(t).
State-space models are common representations of dynamical models. They
describe the same type of linear difference relationship between the inputs and
the outputs as in the ARX model, but they are rearranged so that only one
delay is used in the expressions. To achieve this, some extra variables, the
state variables, are introduced. They are not measured, but can be
reconstructed from the measured input-output data. This is especially useful
when there are several output signals, i.e., when y(t) is a vector. Chapter 3,
“Tutorial”, gives more details about this. For basic use of the toolbox it is
sufficient to know that the order of the state-space model relates to the
number of delayed inputs and outputs used in the corresponding linear
difference equation. The state-space representation looks like
x(t+1)=Ax( t) +Bu (t)+Ke(t)
y(t)=Cx(t)+D u(t)+e(t)
Here x(t) is the vector of state variables. The model order is the dimension of
this vector. The matrix K determines the disturbance properties. Notice that if
K = 0, then the noise source e(t) affects only the output, and no specific model
of the noise properties is built. This corresponds to H = 1 in the general
description above, and is usually referred to as an Output-Error model. Notice
also that D = 0 means that there is no direct influence from u(t) to y(t). Thus
the effect of the input on the output all passes via x(t) and will thus be delayed
at least one sample. The first value of the state variable vector x(0) reflects the
initial conditions for the system at the beginning of the data record. When
dealing with models in state-space form, a typical option is whether to estimate
D, K, and x(0) or to let them be zero.
How to Interpret the Noise Source
In many cases of system identification, the effects of the noise on the output are
insignificant compared to those of the input. With good signal-to-noise ratios
(SNR), it is less important to have an accurate disturbance model.
1-8
Basic Information About Dynamic Models
Nevertheless it is important to understand the role of the disturbances and the
noise source e(t), whether it appears in the ARX model or in the general
descriptions given above.
There are three aspects of the disturbances that should be stressed:
• Understanding white noise
• Interpreting the noise source
• Using the noise source when working with the model
These aspects are discussed one by one.
How can we understand white noise? From a formal point of view, the noise
source e will normally be regarded as white noise. This means that it is entirely
unpredictable. In other words, it is impossible to guess the value of e(t) no
matter how accurately we have measured past data up to time t-1.
How can we interpret the noise source? The actual disturbance contribution to
the output, H e, has real significance. It contains all the influences on the
measured y, known and unknown, that are not contained in the input u. It
explains and captures the fact that even if an experiment is repeated with the
same input, the output signal will typically be somewhat different. However,
the noise source e need not have a physical significance. In the airplane
example mentioned earlier, the disturbance effects are wind gusts and
turbulence. Describing these as arising from a white noise source via a transfer
function H, is just a convenient way of capturing their character.
How can we deal with the noise source when using the model? If the model is
used just for simulation, i.e., the responses to various inputs are to be studied,
then the disturbance model plays no immediate role. Since the noise source e(t)
for new data will be unknown, it is taken as zero in the simulations, so as to
study the effect of the input alone (a noise-free simulation). Making another
simulation with e being arbitrary white noise will reveal how reliable the result
of the simulation is, but it will not give a more accurate simulation result for
the actual system’s response. It is a different thing when the model is used for
prediction: Predicting future outputs from inputs and previously measured
outputs, means that also future disturbance contributions have to be predicted.
A known, or estimated, correlation structure (which really is the disturbance
model) for the disturbances, will allow predictions of future disturbances,
based on the previously measured values.
1-9
1
The System Identification Problem
The need and use of the noise model can be summarized as follows:
• It is, in most cases, required to obtain a better estimate for the dynamics, G.
• It indicates how reliable noise-free simulations are.
• It is required for reliable predictions and stochastic control design.
Terms to Characterize the Model Properties
The properties of an input-output relationship like the ARX model follow from
the numerical values of the coefficients, and the number of delays used. This is
however a fairly implicit way of talking about the model properties. Instead a
number of different terms are used in practice:
Impulse Response
The impulse response of a dynamical model is the output signal that results
when the input is an impulse, i.e., u(t) is zero for all values of t except t=0,
where u(0)=1. It can be computed as in the equation following (ARX), by letting
t be equal to 0, 1, 2, ... and taking y(-T)=y(-2T)=0 and u(0)=1.
Step Response
The step response is the output signal that results from a step input, i.e., u(t)
is zero for negative values of t and equal to one for positive values of t. The
impulse and step responses together are called the model’s transient
response.
Frequency Response
The frequency response of a linear dynamic model describes how the model
reacts to sinusoidal inputs. If we let the input u(t) be a sinusoid of a certain
frequency, then the output y(t) will also be a sinusoid of this frequency. The
amplitude and the phase (relative to the input) will however be different. This
frequency response is most often depicted by two plots; one that shows the
amplitude change as a function of the sinusoid’s frequency and one that shows
the phase shift as function of frequency. This is known as a Bode plot.
1-10
Basic Information About Dynamic Models
Zeros and Poles
The zeros and the poles are equivalent ways of describing the coefficients of a
linear difference equation like the ARX model. The poles relate to the
“output-side” and the zeros relate to the “input-side” of this equation. The
number of poles (zeros) is equal to the number of sampling intervals between
the most and least delayed output (input). In the ARX example in the
beginning of this section, there are consequently two poles and one zero.
1-11
1
The System Identification Problem
The Basic Steps of System Identification
The System Identification problem is to estimate a model of a system based on
observed input-output data. Several ways to describe a system and to estimate
such descriptions exist. This section gives a brief account of the most important
approaches.
The procedure to determine a model of a dynamical system from observed
input-output data involves three basic ingredients:
• The input-output data
• A set of candidate models (the model structure)
• A criterion to select a particular model in the set, based on the information
in the data (the identification method)
The identification process amounts to repeatedly selecting a model structure,
computing the best model in the structure, and evaluating this model’s
properties to see if they are satisfactory. The cycle can be itemized as follows:
1 Design an experiment and collect input-output data from the process to be
identified.
2 Examine the data. Polish it so as to remove trends and outliers, and select
useful portions of the original data. Possibly apply filtering to enhance
important frequency ranges.
3 Select and define a model structure (a set of candidate system descriptions)
within which a model is to be found.
4 Compute the best model in the model structure according to the
input-output data and a given criterion of fit.
5 Examine the obtained model’s properties
6 If the model is good enough, then stop; otherwise go back to Step 3 to try
another model set. Possibly also try other estimation methods (Step 4) or
work further on the input-output data (Steps 1 and 2).
1-12
The Basic Steps of System Identification
The System Identification Toolbox offers several functions for each of these
steps.
For Step 2 there are routines to plot data, filter data, and remove trends in
data, as well as to resample and reconstruct missing data.
For Step 3 the System Identification Toolbox offers a variety of nonparametric
models, as well as all the most common black-box input-output and state-space
structures, and also general tailor-made linear state-space models in discrete
and continuous time.
For Step 4 general prediction error (maximum likelihood) methods, as well as
instrumental variable methods and sub-space methods are offered for
parametric models, while basic correlation and spectral analysis methods are
used for nonparametric model structures.
To examine models in Step 5, many functions allow the computation and
presentation of frequency functions and poles and zeros, as well as simulation
and prediction using the model. Functions are also included for
transformations between continuous-time and discrete-time model
descriptions and to formats that are used in other MATLAB toolboxes, like the
Control System Toolbox and the Signal Processing Toolbox.
1-13
1
The System Identification Problem
A Startup Identification Procedure
There are no standard and secure routes to good models in System
Identification. Given the number of possibilities, it is easy to get confused about
what to do, what model structures to test, and so on. This section describes one
route that often works well, but there are no guarantees. The steps refer to
functions within the GUI, but you can also go through them in command mode.
For the basic commands, see Chapter 4, “Command Reference.”
Step 1: Looking at the Data
Plot the data. Look at them carefully. Try to see the dynamics with your own
eyes. Can you see the effects in the outputs of the changes in the input? Can
you see nonlinear effects, like different responses at different levels, or
different responses to a step up and a step down? Are there portions of the data
that appear to be “messy” or carry no information. Use this insight to select
portions of the data for estimation and validation purposes.
Do physical levels play a role in your model? If not, detrend the data by
removing their mean values. The models will then describe how changes in the
input give changes in output, but not explain the actual levels of the signals.
This is the normal situation.
The default situation, with good data, is that you detrend by removing means,
and then select the first half or so of the data record for estimation purposes,
and use the remaining data for validation. This is what happens when you
apply Quickstart under the pop-up menu Preprocess in the main ident
window.
Step 2: Getting a Feel for the Difficulties
Apply Quickstart under pop-up menu Estimate in the main ident window.
This will compute and display the spectral analysis estimate and the
correlation analysis estimate, as well as a fourth order ARX model with a delay
estimated from the correlation analysis and a default order state-space model
computed by n4sid. This gives three plots. Look at the agreement between the:
• Spectral Analysis estimate and the ARX and state-space models’ frequency
functions
• Correlation Analysis estimate and the ARX and state-space models’
transient responses
1-14
A Startup Identification Procedure
• Measured Validation Data output and the ARX and state-space models’
simulated outputs
If these agreements are reasonable, the problem is not so difficult, and a
relatively simple linear model will do a good job. Some fine tuning of model
orders, and noise models have to be made and you can proceed to Step 4.
Otherwise go to Step 3.
Step 3: Examining the Difficulties
There may be several reasons why the comparisons in Step 2 did not look good.
This section discusses the most common ones, and how they can be handled.
Model Unstable
The ARX or state-space model may turn out to be unstable, but could still be
useful for control purposes. Change to a 5- or 10-step ahead prediction instead
of simulation in the Model Output View.
Feedback in Data
If there is feedback from the output to the input, due to some regulator, then
the spectral and correlations analysis estimates are not reliable. Discrepancies
between these estimates and the ARX and state-space models can therefore be
disregarded in this case. In the Model Residuals View of the parametric
models, feedback in data can also be visible as correlation between residuals
and input for negative lags.
Disturbance Model
If the state-space model is clearly better than the ARX model at reproducing
the measured output, this is an indication that the disturbances have a
substantial influence, and it will be necessary to model them carefully.
Model Order
If a fourth order model does not give a good Model Output plot, try eighth
order. If the fit clearly improves, it follows that higher order models will be
required, but that linear models could be sufficient.
Additional Inputs
If the Model Output fit has not significantly improved by the tests so far, think
over the physics of the application. Are there more signals that have been, or
1-15
1
The System Identification Problem
could be, measured that might influence the output? If so, include these among
the inputs and try again a fourth order ARX model from all the inputs. (Note
that the inputs need not at all be control signals, anything measurable,
including disturbances, should be treated as inputs).
Nonlinear Effects
If the fit between measured and model output is still bad, consider the physics
of the application. Are there nonlinear effects in the system? In that case, form
the nonlinearities from the measured data and add those transformed
measurements as extra inputs. This could be as simple as forming the product
of voltage and current measurements, if you realize that it is the electrical
power that is the driving stimulus in, say, a heating process, and temperature
is the output. This is of course application dependent. It does not take very
much work, however, to form a number of additional inputs by reasonable
nonlinear transformations of the measured ones, and just test if inclusion of
them improves the fit.
Still Problems?
If none of these tests leads to a model that is able to reproduce the Validation
Data reasonably well, the conclusion might be that a sufficiently good model
cannot be produced from the data. There may be many reasons for this. It may
be that the system has some quite complicated nonlinearities, which cannot be
realized on physical grounds. In such cases, nonlinear, black-box models could
be a solution. Among the most used models of this character are the Artificial
Neural Networks (ANN).
Another important reason is that the data simply do not contain sufficient
information, e.g., due to bad signal to noise ratios, large and nonstationary
disturbances, varying system properties, etc.
Otherwise, use the insights of which inputs to use and which model orders to
expect and proceed to Step 4.
Step 4: Fine Tuning Orders and Disturbance
Structures
For real data there is no such thing as a “correct model structure.” However,
different structures can give quite different model quality. The only way to find
this out is to try out a number of different structures and compare the
1-16
A Startup Identification Procedure
properties of the obtained models. There are a few things to look for in these
comparisons.
Fit Between Simulated and Measured Output
Keep the Model Output View open and look at the fit between the model’s
simulated output and the measured one for the Validation Data. Formally, you
could pick that model, for which this number is the highest. In practice, it is
better to be more pragmatic, and also take into account the model complexity,
and whether the important features of the output response are captured.
Residual Analysis Test
You should require of a good model that the cross correlation function between
residuals and input does not go significantly outside the confidence region.
Otherwise there is something in the residuals that originate from the input,
and has not been properly taken care of by the model. A clear peak at lag k
shows that the effect from input u(t-k) on y(t) is not correctly described. A rule
of thumb is that a slowly varying cross correlation function outside the
confidence region is an indication of too few poles, while sharper peaks indicate
too few zeros or wrong delays.
Pole Zero Cancellations
If the pole-zero plot (including confidence intervals) indicates pole-zero
cancellations in the dynamics, this suggests that lower order models can be
used. In particular, if it turns out that the orders of ARX models have to be
increased to get a good fit, but that pole-zero cancellations are indicated, then
the extra poles are just introduced to describe the noise. Then try ARMAX, OE,
or BJ model structures with an A or F polynomial of an order equal to that of
the number of noncanceled poles.
What Model Structures Should be Tested?
Well, you can spend any amount of time to check out a very large number of
structures. It often takes just a few seconds to compute and evaluate a model
in a certain structure, so that you should have a generous attitude to the
testing. However, experience shows that when the basic properties of the
system’s behavior have been picked up, it is not much use to fine tune orders
in absurdum just to press the fit by fractions of percents.
Many ARX models: There is a very cheap way of testing many ARX structures
simultaneously. Enter in the Orders text field many combinations of orders,
1-17
1
The System Identification Problem
using the colon (“:”) notation. You can also press the Order Selection button.
When you select Estimate, models for all combinations (easily several
hundreds) are computed and their (prediction error) fit to Validation Data is
shown in a special plot. By clicking in this plot the best models with any chosen
number of parameters will be inserted into the Model Board, and evaluated as
desired.
Many State-space models: A similar feature is also available for black-box
state-space models, estimated using n4sid. When a good order has been found,
try the PEM estimation method, which often improves on the accuracy.
ARMAX, OE, and BJ models: Once you have a feel for suitable delays and
dynamics orders, if is often useful to try out ARMAX, OE, and/or BJ with these
orders and test some different orders for the disturbance transfer functions (C
and D). Especially for poorly damped systems, the OE structure is suitable.
There is a quite extensive literature on order and structure selection, and
anyone who would like to know more should consult the references.
Multivariable Systems
Systems with many input signals and/or many output signals are called
multivariable. Such systems are often more challenging to model. In particular
systems with several outputs could be difficult. A basic reason for the
difficulties is that the couplings between several inputs and outputs lead to
more complex models. The structures involved are richer and more parameters
will be required to obtain a good fit.
Available Models
The System Identification Toolbox as well as the GUI handle general, linear
multivariable models. All earlier mentioned models are supported in the single
output, multiple input case. For multiple outputs, ARX models and state-space
models are covered. Multi-output ARMAX and OE models are covered via
state-space representations: ARMAX corresponds to estimating the K-matrix,
while OE corresponds to fixing K to zero. (These are pop-up options in the GUI
model order editor.)
Generally speaking, it is preferable to work with state-space models in the
multivariable case, since the model structure complexity is easier to deal with.
It is essentially just a matter of choosing the model order.
1-18
A Startup Identification Procedure
Working with Subsets of the Input-Output Channels
In the process of identifying good models of a system, it is often useful to select
subsets of the input and output channels. Partial models of the system’s
behavior will then be constructed. It might not, for example, be clear if all
measured inputs have a significant influence on the outputs. That is most
easily tested by removing an input channel from the data, building a model for
how the output(s) depends on the remaining input channels, and checking if
there is a significant deterioration in the model output’s fit to the measured
one. See also the discussion under Step 3 above.
Generally speaking, the fit gets better when more inputs are included and often
gets worse when more outputs are included. To understand the latter fact, you
should realize that a model that has to explain the behavior of several outputs
has a tougher job than one that just must account for a single output. If you
have difficulties obtaining good models for a multi-output system, it might be
wise to model one output at a time, to find out which are the difficult ones to
handle.
Models that are just to be used for simulations could very well be built up from
single-output models, for one output at a time. However, models for prediction
and control will be able to produce better results if constructed for all outputs
simultaneously. This follows from the fact that knowing the set of all previous
output channels gives a better basis for prediction, than just knowing the past
outputs in one channel. Also, for systems, where the different outputs reflect
similar dynamics, using several outputs simultaneously will help estimating
the dynamics.
Some Practical Advice
Both the GUI and command line operation will do useful bookkeeping for you,
handling different channels. You could follow the steps of this agenda:
• Import data and create a data set with all input and output channels of
interest. Do the necessary preprocessing of this set in terms of detrending,
etc., and then select a Validation Data set with all channels.
• Then select a Working Data set with all channels, and estimate state-space
models of different orders using n4sid for these data. Examine the resulting
model primarily using the Model Output view.
• If it is difficult to get a good fit in all output channels or you would like to
investigate how important the different input channels are, construct new
1-19
1
The System Identification Problem
data sets using subsets of the original input/output channels. Use the pop-up
menu Preprocess > Select Channels for this. Don’t change the Validation
Data. The GUI will keep track of the input and output channels. It will “do
the right thing” when evaluating the channel-restricted models using the
Validation Data. It might also be appropriate to see if improvements in the
fit are obtained for various model types, built for one output at a time.
• If you decide for a multi-output model, it is often easiest to use state-space
models. Use n4sid as a primary tool and try out pem when a good order has
been found.
1-20
Reading More About System Identification
Reading More About System Identification
There is substantial literature on System Identification. The following
textbook deals with identification methods from a similar perspective as this
toolbox, and also describes methods for physical modeling:
• Ljung L. and T. Glad. Modeling of Dynamic Systems, Prentice Hall,
Englewood Cliffs, N.J. 1994.
For more details about the algorithms and theories of identification:
• Ljung L. System Identification - Theory for the User, Prentice Hall, Upper
Saddle River, N.J. 2nd edition, 1999.
• Söderström T. and P. Stoica. System Identification, Prentice Hall
International, London. 1989.
For more about system and signals:
• Oppenheim J. and A.S. Willsky. Signals and Systems, Prentice Hall,
Englewood Cliffs, N.J. 1985.
The following textbook deals with the underlying numerical techniques for
parameter estimation:
• Dennis, J.E. Jr. and R.B. Schnabel. Numerical Methods for Unconstrained
Optimization and Nonlinear Equations, Prentice Hall, Englewood Cliffs,
N.J. 1983.
1-21
1
The System Identification Problem
1-22
2
The Graphical User
Interface
The Big Picture
. . . . . . . . . . . . . . . . . . 2-2
Handling Data . . . . . . . . . . . . . . . . . . . 2-7
Estimating Models . . . . . . . . . . . . . . . . . 2-15
Examining Models . . . . . . . . . . . . . . . . . 2-28
Some Further GUI Topics . . . . . . . . . . . . . . 2-35
2
The Graphical User Interface
The Big Picture
The System Identification Toolbox provides a graphical user interface (GUI).
The GUI covers most of the toolbox’s functions and gives easy access to all
variables that are created during a session. It is started by typing
ident
in the MATLAB command window.
Figure 2-1: The Main ident Information Window
The Model and Data Boards
System Identification is about data and models and creating models from data.
The main information and communication window ident, is therefore
dominated by two tables:
• A table over available data sets, each represented by an icon
• A table over created models, each represented by an icon
2-2
The Big Picture
These tables will be referred to as the Model Board and the Data Board in this
chapter. You enter data sets into the Data Board by:
• Opening earlier saved sessions.
• Importing them from the MATLAB workspace.
• Creating them by detrending, filtering, selecting subsets, etc., of another
data set in the Data Board.
Imports are handled under the pop-up menu Data while creation of new data
sets is handled under the pop-up menu Preprocess. “Handling Data” on
page 2-7 deals with this in more detail.
The models are entered into the summary board by:
• Opening earlier saved sessions.
• Importing them from the MATLAB workspace.
• Estimating them from data.
Imports are handled under the pop-up menu Models, while all the different
estimation schemes are reached under the pop-up menu Estimate. More about
this in “Estimating Models” on page 2-15.
The Data and Model Boards can be rearranged by dragging and dropping.
More boards open automatically when necessary or when asked for (under
menu Options).
The Working Data
All data sets and models are created from the Working Data set. This is the
data that is given in the center of the ident window. To change the Working
Data set drag and drop any data set from the Data Board on the Working Data
icon.
The Views
Below the Data and Model Boards are buttons for different views. These
control what aspects of the data sets and models you would like to examine, and
are described in more detail in “Handling Data” on page 2-7 and in “Examining
Models” on page 2-28. To select a data set or a model, so that its properties are
displayed, click on its icon. A selected object is marked by a thicker line in the
icon. To deselect, click again. An arbitrary number of data/model objects can be
2-3
2
The Graphical User Interface
examined simultaneously. To have more information about an object,
double-click (or right-click or Ctrl-click) on its icon.
The Validation Data
The two model views Model Output and Model Residuals illustrate model
properties when applied to the Validation Data set. This is the set marked in
the box below these two views. To change the Validation Data, drag and drop
any data set from the Data Board on the Validation Data icon.
It is good and common practice in identification to evaluate an estimated
model’s properties using a “fresh” data set, that is, one that was not used for
the estimation. It is thus good advice to let the Validation Data be different
from the Working Data, but they should of course be compatible with these.
The Work Flow
You start by importing data (under pop-up menu Data); you examine the data
set using the Data Views. You probably remove the means from the data and
select subsets of data for estimation and validation purposes using the items in
the pop-up menu Preprocess. You then continue to estimate models, using the
possibilities under the pop-up menu Estimate, perhaps first doing a
quickstart. You examine the obtained models with respect to your favorite
aspects using the different Model Views. The basic idea is that any checked
view shows the properties of all selected models at any time. This function is
“live” so models and views can be checked in and out at will in an online
fashion. You select/deselect a model by clicking on its icon.
Inspired by the information you gain from the plots, you continue to try out
different model structures (model orders) until you find a model you are
satisfied with.
Management Aspects
Diary: It is easy to forget what you have been doing. By double-clicking on a
data/model icon, a complete diary will be given of how this object was created,
along with other key information. At this point you can also add comments and
change the name of the object and its color.
Layout: To have a good overview of the created models and data sets, it is good
practice to try rearranging the icons by dragging and dropping. In this way
models corresponding to a particular data set can be grouped together, etc. You
2-4
The Big Picture
can also open new boards (Options menu Extra model/data boards) to further
rearrange the icons. These can be dragged across the screen between different
windows. The extra boards are also equipped with notepads for your comments.
Sessions: The Model and Data Boards with all models and data sets together
with their diaries can be saved (under menu item File) at any point, and
reloaded later. This is the counterpart of save/load workspace in the
command-driven MATLAB. The four most recent sessions are listed under File
for immediate open.
Cleanliness: The boards will hold an arbitrary number of models and data sets
(by creating clones of the board when necessary). It is however advisable to
clear (delete) models and data sets that no longer are of interest. Do that by
dragging the object to the Trash Can. (Double-clicking on the trash can will
open it up, and its contents can be retrieved.) Empty the can if you run into
memory problems.
Window Culture: Dialog and plot windows are best managed by the GUI’s
close function (submenu item under File menu, or select Close, or check/
uncheck the corresponding View box). It is generally not suitable to iconify the
windows – the GUI’s handling and window management system is usually a
better alternative.
Workspace Variables
The models and data sets created within the GUI are normally not available in
the MATLAB workspace. Indeed, the workspace is not at all littered with
variables during the sessions with the GUI. The variables can however be
exported at any time to the workspace, by dragging and dropping the object
icon on the To Workspace box. They will then carry the name in the workspace
that marked the object icon at the time of export. You can work with the
variables in the workspace, using any MATLAB commands, and then perhaps
import modified versions back into the GUI. Note that models and data are
exported as the toolbox’s objects idmodel, idfrd, and iddata. For how to
extract information and work with these objects, see Chapter 3, “Tutorial” and
“Model Conversions” on page 4-6 of the “Command Reference “chapter.
The GUI’s names of data sets and models are suggested by default procedures.
Normally, you can enter any other name of your choice at the time of creation
of the variable. Names can be changed (after double-clicking on the icon) at any
time. Unlike the workspace situation, two GUI objects can carry the same
name (i.e., the same string in their icons).
2-5
2
The Graphical User Interface
Help Texts
The GUI contains some 100 help texts that are accessible in a nested fashion,
when required. The main ident window contains general help topics under the
Help menu. This is also the case for the various plot windows. In addition,
every dialog box has a Help push button for current help and advice.
2-6
Handling Data
Handling Data
Data Representation
In the System Identification Toolbox, signals and observed data are
represented as column vectors, e.g.,
u(1)
u(2)
u =
…
…
u(N )
The entry in row number k, i.e., u(k), will then be the signal’s value at sampling
instant number k. It is generally assumed in the toolbox that data are sampled
at equidistant sampling times, and the sampling interval T is supplied as a
specific argument.
We generally denote the input to a system by the letter u and the output by y.
If the system has several input channels, the input data is represented by a
matrix, where the columns are the input signals in the different channels:
u = u1 u2 … um
The same holds for systems with several output channels.
The observed input-output data record is represented in the System
Identification Toolbox by the iddata object, that is created from the input and
output signals by
Data = iddata(y,u,Ts)
where Ts is the sampling time
The iddata object can also be created from the input and output signals when
the data are inserted into the GUI.
2-7
2
The Graphical User Interface
Getting Input-Output Data into the GUI
The information about a data set that should be supplied to the GUI is as
follows:
1 The input and output signals
2 The name you give to the data set
3 The sampling interval
In addition to this mandatory information, you may add further properties that
will help in the bookkeeping:
4 The starting time for the sampling
5 Input and output channel names
6 Input and output channel units
7 Periodicity and intersample behavior of the input
8 Data notes: These are notes for your own information and bookkeeping that
will follow the data and all models created from them.
2-8
Handling Data
As you select the pop-up menu Data and choose the item Import, a dialog box
will open, where you can enter the information items 1 - 8, just listed. This box
has five fields for you to fill in.
Figure 2-2: The Dialog for Importing Data into the GUI
By pressing More, six more fields will become visible.
Input and Output: Enter the variable names of the input and output
respectively. These should be variables in your MATLAB workspace, so you
may have to load some disk files first.
Actually, you can enter any MATLAB expressions in these fields, and they will
be evaluated to compute the input and the output before inserting the data into
the GUI.
Data name: Enter the name of the data set to be used by the GUI. This name
can be changed later on.
Starting time and Sampling interval: Fill these out for correct time and
frequency scales in the plots.
2-9
2
The Graphical User Interface
On the extra page you optionally can fill out
Channel names: Enter strings for the different input and output channels
names. Separate the strings by comma. The number of names must be equal to
the number of channels. If these entries are not filled out, default names, y1,y2,
..., u1, u2 ..., will be used.
Channel units: Enter, in analogous format, the units in which the
measurements are made. These will follow to all models built from data, but
are used only for plot information.
Period: If the input is periodic, enter here the period length. ‘Inf’ means a
non-periodic input, which is default.
Intersample: Choose the intersample behavior of the input as one of ZOH
(zero-order hold, i.e., the input signal piecewise constant between the samples)
or FOH (first-order hold, i.e., the input signal is piecewise linear between the
samples) or BL (Band-limited, i.e., the continuous time input signal has no
power above the Nyquist frequency). ZOH is default.
The box at the bottom is for Notes, where you can enter any text you want to
accompany the data for bookkeeping purposes.
Finally, select Import to insert the data into the GUI. When no more data sets
are to be inserted, select Close to close the dialog box. Reset will empty all the
fields of the box.
The procedure just described will create an iddata object, with all its
properties. If you already have an iddata object available in the workspace,
you can import that directly by selecting the data format Iddata Object in the
pop-up menu at the top of the Import Data dialog.
Taking a Look at the Data
The first thing to do after having inserted the data set into the Data Board is
to examine it. By checking the Data View item Time plot, a plot of the input
and output signals will be shown for the data sets that are selected. You select/
deselect the data sets by clicking on them. For multivariable data, the different
combinations of input and output signals are chosen under menu item
Channel in the plot window. Using the zoom function (drawing rectangles with
the left mouse button down) different portions of the data can be examined in
more detail.
2-10
Handling Data
To examine the frequency contents of the data, check the Data View item Data
spectra. The function is analogous to Time plot, but the signals’ spectra are
shown instead. By default the periodograms of the data are shown, i.e., the
absolute square of the Fourier transforms of the data. The plot can be changed
to any chosen frequency range and a number of different ways of estimating
spectra, by the Options menu item in the spectra window.
The purpose of examining the data in these ways is to find out if there are
portions of the data that are not suitable for identification, if the information
contents of the data is suitable in the interesting frequency regions, and if the
data have to be preprocessed in some way, before using them for estimation.
Preprocessing Data
Detrending
Detrending the data involves removing the mean values or linear trends from
the signals (the means and the linear trends are then computed and removed
from each signal individually). This function is accessed under the pop-up
menu Preprocess, by selecting item Remove Means or Remove Trends. More
advanced detrending, such as removing piecewise linear trends or seasonal
variations cannot be accessed within the GUI. It is generally recommended
that you remove at least the mean values of the data before the estimation
phase. There are however situations when it is not advisable to remove the
sample means. It could for example be that the physical levels are built into the
underlying model, or that integrations in the system must be handled with the
right level of the input being integrated.
Selecting Data Ranges
It is often the case that the whole data record is not suitable for identification,
due to various undesired features (missing or “bad” data, outbursts of
disturbances, level changes etc.), so that only portions of the data can be used.
In any case, it is advisable to select one portion of the measured data for
estimation purposes and another portion for validation purposes. The pop-up
menu item Preprocess > Select Range... opens a dialog box, which facilitates
the selection of different data portions, by typing in the ranges, or marking
them by drawing rectangles with the mouse button down.
For multivariable data it is often advantageous to start by working with just
some of the input and output signals. The menu item Preprocess > Select
2-11
2
The Graphical User Interface
Channels... allows you to select subsets of the inputs and outputs. This is done
in such a way that the input/output numbering and names remains consistent
when you evaluate data and model properties, for models covering different
subsets of the data.
Prefiltering
By filtering the input and output signals through a linear filter (the same filter
for all signals) you can, e.g., remove drift and high frequency disturbances in
the data, that should not affect the model estimation. This is done by selecting
the pop-up menu item Preprocess > Filter... in the main window. The dialog
is quite analogous to that of selecting data ranges in the time domain. You
mark with a rectangle in the spectral plots the intended passband or stop band
of the filter, you select a button to check if the filtering has the desired effect,
and then you insert the filtered data into the GUI’s Data Board.
Prefiltering is a good way of removing high frequency noise in the data, and
also a good alternative to detrending (by cutting out low frequencies from the
pass band). Depending on the intended model use, you can also make sure that
the model concentrates on the important frequency ranges. For a model that
will be used for control design, for example, the frequency band around the
intended closed-loop bandwidth is of special importance.
If you intend to use the data to build models both of the system dynamics and
the disturbance properties, it is recommended to do the filtering at the
estimation phase. That is achieved by selection the pop-up menu item
Estimate > Parametric Models, and then select the estimation Focus to be
Filter. This opens the same filter dialog as above. The prefiltering will
however apply only for estimating the dynamics from input to output. The
disturbance model is determined from the original data.
Resampling
If the data turn out to be sampled too fast, they can be decimated, i.e., every
k-th value is picked, after proper prefiltering (antialias filtering). This is
obtained from menu item Preprocess > Resample.
You can also resample at a faster sampling rate by interpolation, using the
same command, and giving a resampling factor less than one.
2-12
Handling Data
Quickstart
The pop-up menu item Preprocess > Quickstart performs the following
sequence of actions: It opens the Time plot Data view, removes the means
from the signals, and it splits these detrended data into two halves. The first
one is made Working Data and the second one becomes Validation Data. All the
three created data sets are inserted into the Data Board.
Multi-Experiment Data
The toolbox allows the handling of data sets that contain several different
experiments. Both estimation and validation can be applied to such data sets.
This is quite useful to deal with experiments that have been conducted at
different occasions but describe the same system. It is also useful to be able to
keep together pieces of data that have been obtained by cutting out
“informative pieces” from a long data set. Multi-experiment data can be
imported and used in the GUI as any iddata object. Selecting specific part of a
multi-experiment data set is done from the pop-up menu item Preprocess >
Select Experiment. To merge several data sets in the Data board (obtained,
e.g., by cutting out nice portions from other data sets) use the pop-up menu
item Preprocess > Merge Experiment.
Checklist for Data Handling
• Insert data into the GUI’s Data Board.
• Plot the data and examine it carefully.
• Typically detrend the data by removing mean values.
• Select portions of the data for Estimation and for Validation. Drag and drop
these data sets to the corresponding boxes in the GUI.
Simulating Data
The GUI is intended primarily for working with real data sets, and does not
itself provide functions for simulating synthetic data. That has to be done in
command mode, and you can use your favorite procedure in Simulink, the
Signal Processing Toolbox, or any other toolbox for simulation and then insert
the simulated data into the GUI as described above.
The System Identification Toolbox also has several commands for simulation.
For example, should check idinput and sim in the “Command Reference”
chapter for details. The following example shows how the ARMAX model
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The Graphical User Interface
y ( t ) – 1.5y ( t – 1 ) + 0.7y ( t – 2 ) =
u ( t – 1 ) + 0.5u ( t – 2 ) + e ( t ) – e ( t – 1 ) + 0.2e ( t – 1 )
is simulated with a random binary input u.
% Create an ARMAX model
model1 = idpoly([1 -1.5 0.7],[0 1 0.5],[1 -1 0.2]);
u = idinput(400,'rbs',[0 0.3]);
e = randn(400,1);
y = sim(model1,[u e]);
The input, u, and the output, y, can now be imported into the GUI as data, and
the various estimation routines can be applied to them. By also importing the
simulation model, model1, into the GUI, its properties can be compared to those
of the different estimated models.
To simulate a continuous-time state-space model
x· = Ax + Bu + Ke
y = Cx + e
with the same input, and a sampling interval of 0.1 seconds, do the following
in the System Identification Toolbox.
A = [-1 1;-0.5 0]; B = [1; 0.5]; C = [1 0]; D = 0; K = [0.5;0.5];
Model2 = idss(A,B,C,D,K,'Ts', 0) % Ts = 0 means continuous time
Data = iddata([],[u e]);
Data.Ts = 0.1
y=sim(Model2,Data);
2-14
Estimating Models
Estimating Models
The Basics
Estimating models from data is the central activity in the System
Identification Toolbox. It is also the one that offers the most variety of
possibilities and thus is the most demanding one for the user.
All estimation routines are accessed from the pop-up menu Estimate in the
ident window. The models are always estimated using the data set that is
currently in the Working Data box.
One can distinguish between two different types of estimation methods:
• Direct estimation of the Impulse or the Frequency Response of the system.
These methods are often also called nonparametric estimation methods, and
do not impose any structure assumptions about the system, other than that
it is linear.
• Parametric methods. A specific model structure is assumed, and the
parameters in this structure are estimated using data. This opens up a large
variety of possibilities, corresponding to different ways of describing the
system. Dominating ways are state-space and several variants of difference
equation descriptions.
Direct Estimation of the Impulse Response
A linear system can be described by the impulse response g k , with the property
that
∞
y(t) =
∑ gk u ( t – k )
k=1
The name derives from the fact that if the input u(t) is an impulse, i.e., u(t)=1
when t=0 and 0 when t>0, then the output y(t) will be y ( t ) = g t . For a
multivariable system, the impulse response g k will be a ny-by-nu matrix,
where ny is the number of outputs and nu is the number of inputs. Its i-j
element thus describes the behavior of the i-th output after an impulse in the
j-th input.
By choosing menu item Estimate > Correlation Model impulse response
coefficients are estimated directly from the input/output data using so called
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The Graphical User Interface
correlation analysis. The actual method is described under the command
impulse in the “Command Reference” chapter. For a quick action, you can also
just type the letter c in the ident window. This is the hotkey for correlation
analysis.
The resulting impulse response estimate is placed in the Model Board, under
the default name imp. (The name can be changed by double-clicking on the
model icon and then typing in the desired name in the dialog box that opens.)
The best way to examine the result is to select the Model View Transient
Response. This gives a graph of the estimated response. This view offers a
choice between displaying the Impulse or the Step response. For a
multivariable system, the different channels, i.e., the responses from a certain
input to a certain output, are selected under menu item Channel.
The number of lags for which the impulse response is estimated, i.e., the length
of the estimated response, is determined as one of the options in the Transient
Response view.
Direct Estimation of the Frequency Response
The frequency response of a linear system is the Fourier transform of its
impulse response. This description of the system gives considerable
engineering insight into its properties. The relation between input and output
is often written
y(t)=G (z)u(t)+v(t)
where G is the transfer function and v is the additive disturbance. The function
G ( e iωT )
as a function of (angular) frequency ω is then the frequency response or
frequency function. T is the sampling interval. If you need more details on the
different interpretations of the frequency response, See “The System
Identification Problem” on page 3-9 in the Tutorial chapter or any textbook on
linear systems.
The system’s frequency response is directly estimated using spectral analysis
by the menu item Estimate > Spectral Model, and then selecting the
Estimate button in the dialog box that opens. The result is placed on the Model
Board under the default name spad. The best way to examine it is to plot it
using the Model View Frequency Response. This view offers a number of
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Estimating Models
different options on how to graph the curves. The frequencies for which to
estimate the response can also be selected as an option under the Options
menu in this View window.
The Spectral Analysis command also estimates the spectrum of the additive
disturbance v(t) in the system description. This estimated disturbance
spectrum is examined under the Model View item Noise Spectrum.
The Spectral Analysis estimate is stored as an idfrd object. If you need to
further work with the estimates, you can export the model to the MATLAB
workspace and retrieve the responses directly from this object or by Nyquist
and Bode. See idfrd, bode, and nyquist in the “Command Reference” chapter
for more information. (A model is exported by dragging and dropping it over the
To Workspace icon.)
Two options that affect the spectral analysis estimate can be set in the dialog
box. The most important choice is a number, M, (the size of the lag window)
that affects the frequency resolution of the estimates. Essentially, the
frequency resolution is about 2 π /M radians/(sampling interval). The choice of
M is a trade-off between frequency resolution and variance (fluctuations). A
large value of M gives good resolution but fluctuating and less reliable
estimates. The default choice of M is good for systems that do not have very
sharp resonances and may have to be adjusted for more resonant systems.
The options also offer a choice between the Blackman-Tukey windowing
method spa (which is default) and a method based on smoothing direct Fourier
transforms, etfe. etfe has an advantage for highly resonant systems, in that
it is more efficient for large values of M. It however has the drawbacks that it
requires linearly spaced frequency values, does not estimate the disturbance
spectrum, and does not provide confidence intervals. The actual methods are
described in more detail in the “Command Reference” chapter under spa and
etfe. To obtain the spectral analysis model for the current settings of the
options, you can just type the hotkey s in the ident window.
Estimation of Parametric Models
The System Identification Toolbox supports a wide range of model structures
for linear systems. They are all accessed by the menu item Estimate >
Parametric Models... in the ident window. This opens up a dialog box
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The Graphical User Interface
Parametric Models, which contains the basic dialog for all parametric
estimation as shown below.
Figure 2-3: The Dialog Box for Estimating Parametric Models
The basic function of this box is as follows.
As you select Estimate, a model is estimated from the Working Data. The
structure of this model is defined by the pop-up menu Structure together with
the edit box Orders. It is given a name, which is written in the edit box Name.
The GUI will always suggest a default model name in the Name box, but you
can change it to any string before selecting the Estimate button. (If you intend
to export the model later, avoid spaces in the name.)
The interpretation of the model structure information (typically integers) in
the Order box, depends on the selected Structure in the pop-up menu. This
covers, typically, six choices:
• ARX models
• ARMAX model
• Output-Error (OE) models
• Box-Jenkins (BJ) models
• State-space models
• Model structure defined by Initial Model (User defined structures)
These are dealt with one by one shortly.
2-18
Estimating Models
You can fill out the Order box yourself at any time, but for assistance you can
select Order Editor. This will open up another dialog box, depending on the
chosen Structure, in which the desired model order and structure information
can be entered in a simpler fashion.
You can also enter a name of a MATLAB workspace variable in the order edit
box. This variable should then have a value that is consistent with the
necessary orders for the chosen structure.
Note For the state-space structure and the ARX structure, several orders
and combination of orders can be entered. Then all corresponding models will
be compared and displayed in a special dialog window for you to select
suitable ones. This could be a useful tool to select good model orders. This
option is described in more detail later in this section. When it is available, a
button Order selection is visible.
Estimation Method
A common and general method of estimating the parameters is the prediction
error approach, where simply the parameters of the model are chosen so that
the difference between the model’s (predicted) output and the measured output
is minimized. This method is available for all model structures. Except for the
ARX case, the estimation involves an iterative, numerical search for the best
fit.
To obtain information from and interact with this search, select Iteration
control... This is a button which is visible when an iterative estimation process
has been selected. This also gives access to a number of options that govern the
search process. (See “Algorithm Properties” on page 4-10 in the “Command
Reference” chapter.)
For some model structures (the ARX model, and black-box state-space models)
methods based on correlation are also available: Instrumental Variable (IV)
and Sub-space (N4SID) methods. The choice between methods is made in the
Parametric Models dialog box.
The dialog box also has three pop-up menus that offer further options: Focus
allows you to choose between a frequency weighting that concentrates on the
model’s prediction or simulation performance. Another alternative is
prefiltering, which was described on page 2-12. Moreover, the pop-up menu
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The Graphical User Interface
InitialState gives options to estimate the initial state or to fix it to zero. The
value Auto makes an automatic choice among these options. Finally, the menu
Covariance allows the choice between Estimate and None. The normal
situation is that the covariance of the model is estimated, so that various
uncertainty measures can be displayed in the plots. However, for high order
state-space models estimated by N4SID, or large multivariable ARX models,
the computation of the covariance matrix may take quite a long time. Choosing
Covariance: None will then greatly reduce the computation time.
Resulting Models
The estimated model is inserted into the GUI’s Model Board. You can then
examine its various properties and compare them with other models’ properties
using the Model View plots. More about that in “Examining Models” on
page 2-28.
To take a look at the model itself, double-click on the model’s icon (or right-click
or Ctrl-click). The Data/Model Info window that then opens gives you
information about how the model was estimated. You can then also select the
Present button, which will list the model, and its parameters with estimated
standard deviations in the MATLAB command window.
If you need to work further with the model, you can export it by dragging and
dropping it over the To Workspace icon, and then apply any MATLAB and
toolbox commands to it. (See, in particular, the commands ssdata, tfdata, d2c,
and get in the “Command Reference” chapter.)
How to Know Which Structure and Method to Use
There is no simple way to find out “the best model structure”; in fact, for real
data, there is no such thing as a best structure. Some routes to find good and
acceptable model are described in “A Startup Identification Procedure” on
page 1-14. It is best to be generous at this point. It often takes just a few
seconds to estimate a model, and by the different validation tools described in
the next section, you can quickly find out if the new model is any better than
the ones you had before. There is often a significant amount of work behind the
data collection, and spending a few extra minutes trying out several different
structures is usually worthwhile.
2-20
Estimating Models
ARX Models
The Structure
The most used model structure is the simple linear difference equation
y ( t ) + a 1 y ( t – 1 ) + … + a na y ( t – na ) =
b 1 u ( t – nk ) + … + b nb u ( t – nk – nb + 1 )
which relates the current output y(t) to a finite number of past outputs y(t-k)
and inputs u(t-k).
The structure is thus entirely defined by the three integers na, nb, and nk. na
is equal to the number of poles and nb–1 is the number of zeros, while nk is the
pure time-delay (the dead-time) in the system. For a system under
sampled-data control, typically nk is equal to 1 if there is no dead-time.
For multi-input systems nb and nk are row vectors, where the i-th element
gives the order/delay associated with the i-th input.
Entering the Order Parameters
The orders na, nb, and nk can either be directly entered into the edit box Orders
in the Parametric Models window, or selected using the pop-up menus in the
Order Editor.
Estimating Many Models Simultaneously
By entering any or all of the structure parameters as vectors, using MATLAB’s
colon notation, like na=1:10, etc., you define many different structures that
correspond to all combinations of orders. When selecting Estimate, models
corresponding to all of these structures are computed. A special plot window
will then open that shows the fit of these models to Validation Data. By clicking
in this plot, you can then enter any models of your choice into the Model Board.
Multi-input models: For multi-input models you can of course enter each of
the input orders and delays as a vector. The number of models resulting from
all combinations of orders and delays can however be very large. As an
alternative, you may enter one vector (like nb=1:10) for all inputs and one
vector for all delays. Then only such models are computed that have the same
orders and delays from all inputs.
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The Graphical User Interface
Estimation Methods
There are two methods to estimate the coefficients a and b in the ARX model
structure:
Least Squares: Minimizes the sum of squares of the right-hand side minus the
left-hand side of the expression above, with respect to a and b. This is obtained
by selecting ARX as the Method.
Instrumental Variables: Determines a and b so that the error between the
right- and left- hand sides becomes uncorrelated with certain linear
combinations of the inputs. This is obtained by selecting IV in the Method box.
The methods are described in more detail in the “Command Reference” chapter
under arx and iv4.
Multi-Output Models
For a multi-output ARX structure with ny outputs and nu inputs, the
difference equation above is still valid. The only change is that the coefficients
a are ny-by-ny matrices and the coefficients b are ny-by-nu matrices.
The orders [NA NB NK] define the model structure as follows:
NA: an ny-by-ny matrix whose i-j entry is the order of the polynomial (in the
delay operator) that relates the j-th output to the i-th output
NB: an ny-by-nu matrix whose i-j entry is the order of the polynomial that
relates the j-th input to the i-th output
NK: an ny-by-nu matrix whose i-j entry is the delay from the j-th input to the
i-th output
The Order Editor dialog box allows the choices
NA = na∗ones(ny,ny)
NB = nb∗ones(ny,nu)
NK = nk∗ones(ny,nu)
where na, nb, and nk are chosen by the pop-up menus.
For tailor-made order choices, construct a matrix [NA NB NK] in the MATLAB
command window and enter the name of this matrix in the Order edit box in
the Parametric Models window.
Note that the possibility to estimate many models simultaneously is not
available for multi-output ARX models.
2-22
Estimating Models
See “Defining Model Structures” on page 3-35 for more information on
multi-output ARX models.
ARMAX, Output-Error and Box-Jenkins Models
There are several elaborations of the basic ARX model, where different
disturbance models are introduced. These include well known model types,
such as ARMAX, Output-Error, and Box-Jenkins.
The General Structure
A general input-output linear model for a single-output system with input u
and output y can be written
nu
A ( q )y ( t ) =
∑ [ B i ( q ) ⁄ Fi ( q ) ] ui ( t – nki ) + [ C ( q ) ⁄ D ( q ) ] e ( t )
i=1
Here ui denotes input #i, and A, Bi, C, D, and Fi, are polynomials in the shift
operator (z or q). (Don’t get intimidated by this: It is just a compact way of
writing difference equations; see below.)
The general structure is defined by giving the time-delays nk and the orders of
these polynomials (i.e., the number of poles and zeros of the dynamic model
from u to y, as well as of the disturbance model from e to y).
The Special Cases
Most often the choices are confined to one of the following special cases.
ARX: A ( q )y ( t ) = B ( q )u ( t – nk ) + e ( t )
ARMAX: A ( q )y ( t ) = B ( q )u ( t – nk ) + C ( q )e ( t )
OE:
BJ:
y ( t ) = [ B ( q ) ⁄ F ( q ) ]u ( t – nk ) + e ( t ) (Output-Error)
y ( t ) = [ B ( q ) ⁄ F ( q ) ]u ( t – nk ) + [ C ( q ) ⁄ D ( q ) ]e ( t ) (Box-Jenkins)
The “shift operator polynomials” are just compact ways of writing difference
equations. For example the ARMAX model in longhand would be
y ( t ) + a 1 y ( t – 1 ) + … + a na y ( t – na ) = b 1 u ( t – nk ) + … +
b nb u ( t – nk – nb + 1 ) + e ( t ) + c 1 e ( t – 1 ) + … + c nc e ( t – nc )
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Note that A(q) corresponds to poles that are common between the dynamic
model and the disturbance model (useful if disturbances enter the system
“close to” the input). Likewise F i ( q ) determines the poles that are unique for
the dynamics from input # i, and D(q) the poles that are unique for the
disturbances.
The reason for introducing all these model variants is to provide for flexibility
in the disturbance description and to allow for common or different poles
(dynamics) for the different inputs.
Entering the Model Structure
Use the Structure pop-up menu in the Parametric Models dialog to choose
between the ARX, ARMAX, Output-Error, and Box-Jenkins structures. Note
that if the Working Data set has several outputs, only the first choice is
available. For time series (data with no input signal) only AR and ARMA are
available among these choices. These are the time series counterparts of ARX
and ARMAX.
The orders of the polynomials are selected by the pop-up menus in the Order
Editor dialog window, or by directly entering them in the edit box Orders in
the Parametric Models window. When the order editor is open, the default
orders, entered as you change the model structure, are based on previously
used orders.
Estimation Method
The coefficients of the polynomials are estimated using a prediction error/
Maximum Likelihood method, by minimizing the size of the error term “e” in
the expression above. Several options govern the minimization procedure.
These are accessed by activating Iteration Control in the Parametric Models
window, and selecting Options.
The algorithms are further described in Chapter 4, “Command Reference”
under armax, Algorithm Properties, bj, oe, and pem. See also “Parametric
Model Estimation” on page 3-25 and “Defining Model Structures” on page 3-35.
2-24
Estimating Models
Note These model structures are available only for the scalar output case.
For multi-output models, the state-space structures offer the same flexibility.
Also note that it is not possible to estimate many different structures
simultaneously for the input-output models.
State-Space Models
The Model Structure
The basic state-space model in innovations form can be written
x(t+1) = A x( t) + B u(t) + K e( t)
y (t) = C x (t) + D u(t) + e( t)
The System Identification Toolbox supports two kinds of parametrizations of
state-space models: black-box, free parametrizations, and parametrizations
tailor-made to the application. The latter is discussed below under the heading
“User Defined Model Structures” on page 2-26. First we will discuss the
black-box case.
Entering Black-Box State-Space Model Structures
The most important structure index is the model order; i.e., the dimension of
the state vector x.
Use the pop-up menu in the Order Editor to choose the model order, or enter
it directly into the Orders edit box in the Parametric Models window. Using
the other pop-up menus in the Order Editor, you can further affect the chosen
model structure:
• Fixing K to zero gives an Output-Error method; i.e., the difference between
the model’s simulated output and the measured one is minimized. Formally,
this corresponds to an assumption that the output disturbance is white
noise.
The delays from the input can be chosen independently for each input. It will
be a row vector nk, with nu entries. When the delay is larger than or equal to
one, the D-matrix in the discrete time model is fixed to zero. For physical
systems, without a pure time delay, that are driven by piece-wise constant
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2
The Graphical User Interface
inputs, nk = 1 is a natural assumption This is also the default. Note also that
the delays can be directly entered into the Orders edit box.
Estimating Many Models Simultaneously
By entering a vector for the model order, using MATLAB’s colon notation, (such
as “1:10”) all indicated orders will be computed using a preliminary method.
You can then enter models of different orders into the Model Board by clicking
in a special graph that contains information about the models.
Estimation Methods
There are two basic methods for the estimation:
PEM: Is a standard prediction error/maximum likelihood method, based on
iterative minimization of a criterion. The iterations are started up at
parameter values that are computed from n4sid. The parametrization of the
matrices A, B, C, D, and K is free. The search for minimum is controlled by a
number of options. These are accessed from the Option button in the Iteration
Control window.
N4SID: Is a subspace-based method that does not use iterative search. The
quality of the resulting estimates may significantly depend some options called
N4Weight and N4Horizon. These options can be chosen in the Order Editor
window. If N4Horizon is entered with several rows, the models corresponding
to the horizons in each row are examined separately using the Working data.
The best model in terms of prediction (or simulation, if K = 0) performance is
selected. A figure is shown that illustrates the fit as a function of the horizon.
If the N4Horizon box is left empty, a default choice is made.
See n4sid and pem in the “Command Reference” chapter for more information.
User Defined Model Structures
State-Space Structures
The System Identification Toolbox supports user-defined linear state-space
models of arbitrary structure. Using the idmodel idss, known and unknown
parameters in the A, B, C, D, K, and X0 matrices can be easily defined both for
discrete- and continuous-time models. The idgrey object allows you to use a
completely arbitrary greybox structure, defined by an M-file. The model object
properties can be easily manipulated. See idss and idgrey in Chapter 4,
2-26
Estimating Models
“Command Reference” and “Structured State-Space Models with Free
Parameters: the idss Model” on page 3-42
To use these structures in conjunction with the GUI, just define the
appropriate structure in the MATLAB command window. Then use the
Structure pop-up menu to select By Initial Model and enter the variable
name of the structure in the edit box Initial Model in the Parametric Models
window and select Estimate.
Any Model Structure
Arbitrary model structures can be defined using the System Identification
Toolbox model objects:
• idpoly: Creates Input-output structures for single-output models
• idss: Creates Linear State-space models with arbitrary, free parameters
• idgrey: Creates completely arbitrary parametrizations of linear systems
• idarx: Creates multivariable ARX structures
In addition, all estimation commands create model structures in terms of the
resulting models.
Enter the name of any model structure in the box Orders (or Initial model) in
the window Parametric Models and then select Estimate. Then the
parameters of the model structure are adjusted to the chosen Working Data
set. The method is a standard prediction error/maximum likelihood approach
that iteratively searches for the minimum of a criterion. Options that govern
this search are accessed by the Option button in the Iteration Control
window.
The name of the initial model must be a variable either in the workspace or in
the Model Board. In the latter case you can just drag and drop it over the
Orders/Initial model edit box.
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The Graphical User Interface
Examining Models
Having estimated a model is just a first step. It must now be examined,
compared with other models, and tested with new data sets. This is primarily
done using the six Model View functions, at the bottom of the main ident
window:
• Frequency response
• Transient response
• Zeros and poles
• Noise spectrum
• Model output
• Model residuals
In addition, you can double-click on the model’s icon to get Text Information
about the model. Finally, you can export the model to the MATLAB workspace
and use any commands for further analysis and model use.
Views and Models
The basic idea is that if a certain View window is open (checked), then all
models in the Model Summary Board that are selected will be represented in
the window. The curves in the View window can be clicked in and out by
selecting and deselecting the models in an online fashion. You select and
deselect a model by clicking on its icon. An selected model is marked with a
thicker line in its icon.
On color screens, the curves are color coded along with the model icons in the
Model Board. Before printing a plot it might be a good idea to separate the line
styles (menu item under Style). This could also be helpful on black and white
screens.
Note that models that are obtained by spectral analysis only can be
represented as frequency response and noise spectra, and that models
estimated by correlation analysis only can be represented as transient
response.
2-28
Examining Models
The Plot Windows
The six views all give similar plot windows, with several common features.
They have a common menu bar, which covers some basic functions.
First of all, note that there is a zoom function in the plot window. By dragging
with the left mouse button down, you can draw rectangles, which will be
enlarged when the mouse button is released. By double-clicking, the original
axis scales are restored. For plots with two axes, the x-axes scales are locked to
each other. A single click on the left mouse button zooms in by a factor of two,
while the middle button zooms out. The zoom function can be deactivated if
desired. Just select the menu item Zoom under Style.
Second, by pointing to any curve in the plot, and pressing the right mouse
button, the curve will be identified with model name and present coordinates.
The common menu bar covers the following functions.
File
File allows you to copy the current figure to another, standard MATLAB figure
window. This might be useful, e.g., when you intend to print a customized plot.
Other File items cover printing the current plot and closing the plot window.
Options
Options first of all cover actions for setting the axes scaling. This menu item
also gives a number of choices that are specific for the plot window in question,
like a choice between step response or impulse response in the Transient
response window.
A most important option is the possibility to show confidence intervals. Each
estimated model property has some uncertainty. This uncertainty can be
estimated from data. By checking Show confidence intervals, a confidence
region around the nominal curve (model property) will be marked (by
dash-dotted lines). The level of confidence can also be set under this menu item.
Note Confidence intervals are supported for most models and properties,
except models estimated using etfe, and the k-step ahead prediction-property.
For n4sid, the covariance properties are actually not fully known. The
Cramer-Rao lower limit for the covariance matrix is then used instead.
2-29
2
The Graphical User Interface
Style
The style menu gives access to various ways of affecting the plot. You can add
gridlines, turn the zoom on and off, and change the linestyles. The menu also
covers a number of other options, like choice of units and scale for the axis.
Channel
For multivariate systems, you can choose which input-output channel to
examine. The current choice is marked in the figure title.
Help
The Help menu has a number of items, which explain the plot and its options.
Frequency Response and Disturbance Spectra
All linear models that are estimated can be written in the form
y(t)=G (z)u(t)+v(t)
where G(z) is the (discrete-time) transfer function of the system and v(t) is an
additive disturbance. The frequency response or frequency function of the
iωT
) viewed as a function of angular
system is the complex-valued function G ( e
frequency ω.
This function is often graphed as a Bode diagram, i.e., the logarithm of the
iωT
) as well as the phase (the argument)
amplitude (the absolute value) of G ( e
iωT
) are plotted against the logarithm of frequency ω in two separate
of G ( e
plots. These plots are obtained by checking the Model View Frequency
Response in the main ident window.
The estimated spectrum of the disturbance v is plotted as a power spectrum by
choosing the Model View Noise Spectrum.
If the data is a time series y (with no input u), then the spectrum of y is plotted
under Noise Spectrum, and no frequency functions are given.
Transient Response
Good and simple insight into a model’s dynamic properties is obtained by
looking at its step response or impulse response. This is the output of the model
when the input is a step or an impulse. These responses are plotted when the
Model View Transient Response is checked.
2-30
Examining Models
It is quite informative to compare the transient response of a parametric
model, with the one that was estimated using correlation analysis. If there is
good agreement between the two, you can be quite confident that some
essentially correct features have been picked up. It is useful to check the
confidence intervals around the responses to see what “good agreement” could
mean quantitatively.
Many models provide a description of the additive disturbance v(t).
v(t)=H( z)e( t)
Here H(z) is a transfer function that describes how the disturbance v(t) can be
thought of as generated by sending white noise e(t) through it. To display the
properties of H, you can choose channels (in the Channel menu) that have
noise components as inputs. The names of these channels are like [email protected], for
the noise component that directly affects the output with name ynam.
Poles and Zeros
The poles of a system are the roots of the denominator of the transfer function
G(z), while the zeros are the roots of the numerator. In particular the poles
have a direct influence on the dynamic properties of the system.
The poles and zeros of G (and H ) are plotted by choosing the Model View Poles
and Zeros.
It is useful to turn on the confidence intervals in this case. They will clearly
reveal which poles and zeros could cancel (their confidence regions overlap).
That is an indication that a lower order dynamic model could be used.
For multivariable systems it is the poles and zeros of the individual input/
output channels that are displayed. To obtain the so called transmission zeros,
you will have to export the model and then apply the command tzero, provided
you have the Control Systems Toolbox.
Compare Measured and Model Output
A very good way of obtaining insight into the quality of a model is to simulate
it with the input from a fresh data set, and compare the simulated output with
the measured one. This gives a good feel for which properties of the system
have been picked up by the model, and which haven’t.
This test is obtained by checking the Model View Model Output. Then the
data set currently in the Validation Data box will be used for the comparison.
2-31
2
The Graphical User Interface
The fit will also be displayed. This is computed as the percentage of the output
variation that is reproduced by the model. So, a model that has a fit of 0% gives
the same mean square error as just setting the model output to be the mean of
the measured output.
If the model is unstable, or has integration or very slow time constants, the
levels of the simulated and the measured output may drift apart, even for a
model that is quite good (at least for control purposes). It is then a good idea to
evaluate the model’s predicted output rather than the simulated one. With a
prediction horizon of k, the k-step ahead predicted output is then obtained as
follows:
The predicted value y(t) is computed from all available inputs u ( s ) ( s ≤ t ) (used
according to the model) and all available outputs up to time t-k, y ( s ) ( s ≤ t – k ) .
The simulation case, where no past outputs at all are used, thus formally
corresponds to k=∞. To check if the model has picked up interesting dynamic
properties, it is wise to let the predicted time horizon (kT, T being the sampling
interval) be larger than the important time constants.
Note here that different models use the information in past output data in their
predictors in different ways. This depends on the disturbance model. For
example, so called Output-Error models (obtained by fixing K to zero for
state-space models and setting na=nc=nd=0 for input output models, see the
previous section) do not use past outputs at all. The simulated and the
predicted outputs, for any value of k, thus coincide.
Residual Analysis
In a model
y ( t ) = G ( z )u ( t ) + H ( z )e ( t )
the noise source e(t) represents that part of the output that the model could not
reproduce. It gives the “left-overs” or, in Latin, the residuals. For a good model,
the residuals should be independent of the input. Otherwise, there would be
more in the output that originates from the input and that the model has not
picked up.
To test this independence, the cross-correlation function between input and
residuals is computed by checking the Model View Model Residuals. It is wise
to also display the confidence region for this function. For an ideal model the
correlation function should lie entirely between the confidence lines for positive
2-32
Examining Models
lags. If, for example, there is a peak outside the confidence region for lag k, this
means that there is something in the output y(t) that originates from u(t-k) and
that has not been properly described by the model. The test is carried out using
the Validation Data. If these were not used to estimate the model, the test is
quite tough. See also “Model Structure Selection and Validation” on page 3-63.
For a model also to give a correct description of the disturbance properties (i.e.,
the transfer function H), the residuals should be mutually independent. This
test is also carried out by the view Model Residuals, by displaying the
auto-correlation function of the residuals (excluding lag zero, for which this
function by definition is 1). For an ideal model, the correlation function should
be entirely inside the confidence region.
Text Information
By double-clicking (right mouse button or Ctrl-click) on the model icon, a
Data/model Info dialog box opens, which contains some basic information
about the model. It also gives a diary of how the model was created, along with
the notes that originally were associated with the estimation data set. At this
point you can do a number of things:
Present
Selecting the Present button displays details of the model in the MATLAB
command window. The model’s parameters along with estimated standard
deviations are displayed, as well as some other notes.
Modify
You can simply type in any text you want anywhere in the Diary and Notes
editable text field of the dialog box. You can also change the name of the model
just by editing the text field with the model name. The color, which the model
is associated with in all plots, can also be edited. Enter RGB-values or a color
name (like 'y') in the corresponding box.
LTI Viewer
If you have the Control System Toolbox, you will see an icon To LTI Viewer in
the main window. By dragging and dropping a model onto this icon you will
open the LTI Viewer. This viewer handles an arbitrary amount of models, but
it requires all of them to have the same number of inputs and outputs.
2-33
2
The Graphical User Interface
Further Analysis in the MATLAB Workspace
Any model and data object can be exported to the MATLAB workspace by
dragging and dropping its icon over the To Workspace box in the ident
window.
Once you have exported the model to the workspace, there are many commands
by which you can further transform it, examine it, and convert it to other
formats for use in other toolboxes. Some examples of such commands are
d2c
Transform to continuous time
ss, idss,
ssdata
Convert to state-space representation
tf, tfdata
Convert to transfer function form
zpk, zpkdata
Convert to zeros and poles
Note that the commands ss, tf, and zkp transform the model to the Control
System Toolbox’s LTI models. Moreover, if you have that toolbox many of its
LTI-commands can be applied directly to the model objects of the Identification
Toolbox. See “Connections Between the Control System Toolbox and the
System Identification Toolbox” on page 3-87
Also, if you need to prepare specialized plots that are not covered by the Views,
all the System Identification Toolbox commands for computing and extracting
simulations, frequency functions, zeros and poles, etc., are available. See the
“Tutorial” and “Command Reference” chapters.
2-34
Some Further GUI Topics
Some Further GUI Topics
This section discusses a number of different topics.
Mouse Buttons and Hotkeys
The GUI uses three mouse buttons. If you have fewer buttons on your mouse,
the actions associated with the middle and right mouse buttons are obtained
by shift-click, alt-click or control-click, depending on the computer.
The Main ident Window
In the main ident window the mouse buttons are used to drag and drop, to
select/deselect models and data sets, and to double-click to get text information
about the object. You can use the left mouse button for all of this. A certain
speed-up is obtained if you use the left button for dragging and dropping, the
middle one for selecting models and data sets, and the right one for
double-clicking (actually for the right button, (Ctrl-click) a single click is
sufficient). On a slow machine a double-click from the left button might not be
recognized.
The ident window also has a number of hotkeys. By pressing a keyboard letter
when it is the current window, some functions can be quickly activated. These
are:
• s: Computes Spectral Analysis Model using the current options settings.
(These can be changed in the dialog window that opens when you choose
Spectral Model in the Estimate pop-up menu.)
• c: Computes Correlation Analysis Model using the current options
settings.
• q: Computes the models associated with the Quickstart.
• d: Opens a dialog window for importing Data
Plot Windows
In the various plot windows the action of the mouse buttons depends on
whether the zoom is activated or not:
Zoom Active: Then the left and middle mouse buttons are associated with the
zoom functions as in the standard MATLAB zoom. Left button zooms in and the
2-35
2
The Graphical User Interface
middle one zooms out. In addition, you can draw rectangles with the left
button, to define the area to be zoomed. Double-clicking restores the original
plot. The right mouse button is associated with special GUI actions that depend
on the window. In the View plots, the right mouse button is used to identify the
curves. Point and click on a curve, and a box will display the name of the model/
data set that the curve is associated with, and also the current coordinate
values for the curve. In the Model Selection plots the right mouse button is
used to inspect and select the various models. In the Prefilter and Data Range
plots, rectangles are drawn with this mouse button down, to define the selected
range.
Zoom not active: The special GUI functions just mentioned are obtained by
any mouse button.
The zoom is activated and deactivated under the menu item Style. The default
setting differs between the plots. Don’t activate the zoom from the command
line! That will destroy the special GUI functions. (If you happen to do so
anyway, “quit” the window and open it again.)
Troubleshooting in Plots
The function Auto-range, which is found under the menu item Options, sets
automatic scales to the plots. It is also a good function to invoke when you think
that you have lost control over the curves in the plot. (This may happen, for
example, if you have zoomed in a portion of a plot and then change the data of
the plot).
If the view plots don’t respond the way you expect them to, you can always
“quit” the window and open it again. By quit here we mean using the
underlying window system’s own quitting mechanism, which is called different
things in the different platforms. The normal way to close a window is to use
the Close function under the menu item File, or to uncheck the corresponding
check box.
Layout Questions and idprefs.mat
The GUI comes with a number of preset defaults. These include the window
sizes and positions, the colors of the different models, and the default options
in the different View windows.
The window sizes and positions, as well as the options in the plot windows, can
of course be changed during the session in the standard way. If you want the
2-36
Some Further GUI Topics
GUI to start with your current window layout and current plot options, select
menu item
Options > Save preferences
in the main ident window. This saves the information in a file idprefs.mat.
This file also stores information about the four most recent sessions with ident.
This allows the session File menu to be correctly initialized. The session
information is automatically stored upon exit. The layout and preference
information is only saved when the indicated option is selected.
The file idprefs.mat is created the first time you close the GUI. It is by default
stored in the same directory as your startup.m file. If this default does not
work, you are prompted for a directory where to store the file. This can be
ignored, but then session and preference information cannot be saved.
To change or select a directory for idprefs.mat, use the command midprefs.
See the “Command Reference” chapter for details.
To change model colors and default options to your own customized choice,
make a copy of the M-file idlayout.m to your own directory (which should be
before the basic ident directory in the MATLABPATH), and edit it according to its
instructions.
Customized Plots
If you need to prepare hardcopies of your plots with specialized texts, titles and
so on, make a copy of the figure first, using Copy Figure under the File menu
item. This produces a copy of the current figure in a standard MATLAB figure
format.
For plots that are not covered by the View windows, (e.g., Nyquist plots), you
have to export the model to the MATLAB workspace and construct the plots
there.
What Cannot be Done Using the GUI
The GUI covers primarily everything you would like to do to examine data,
estimate models and evaluate and compare models. It does not cover:
• Generation (simulation) of data sets
• Model creation (other than by estimation)
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2
The Graphical User Interface
• Model manipulation and conversions
• Recursive (on-line) estimation algorithms
To see what M-files are available in the toolbox for these functions, check the
“Tutorial” chapter, as well as the “Simulation and Prediction”, “Model
Structure Creation”, “Manipulating Model Structures”, “Model Conversions”,
and “Recursive Parameter Estimation” tables in the beginning of the
“Command Reference” chapter.
Note that at any point you can export a data set or a model to the MATLAB
workspace (by dragging and dropping its icon on the To Workspace icon).
There you can modify and manipulate it any way you want and then import it
back into ident. You can, for example, construct a continuous-time model from
an estimated discrete-time one (using d2c), and then use the model views to
compare the two.
2-38
3
Tutorial
Overview
. . . . . . . . . . . . . . . . . . . . . 3-2
The Toolbox Commands . . . . . . . . . . . . . . . 3-3
An Introductory Example to Command Mode . . . . . 3-5
The System Identification Problem
. . . . . . . . . 3-9
Data Representation and Nonparametric Model
Estimation . . . . . . . . . . . . . . . . . . 3-18
Parametric Model Estimation . . . . . . . . . . . . 3-25
Defining Model Structures
. . . . . . . . . . . . . 3-35
Examining Models . . . . . . . . . . . . . . . . . 3-49
Model Structure Selection and Validation
Dealing with Data
. . . . . . 3-63
. . . . . . . . . . . . . . . . . 3-74
Recursive Parameter Estimation
Some Special Topics
. . . . . . . . . . 3-78
. . . . . . . . . . . . . . . . 3-85
3
Tutorial
Overview
This chapter has three purposes:
• It gives an overview of System Identification theory, the basic models and
disturbance descriptions used, and the character of the basic algorithms. It
also provides some practical advice for a number of issues that are essential
for a successful application.
• It describes the commands and objects of the System Identification Toolbox,
their syntax and use. If you primarily use the graphical user interface (GUI),
you will not have to bother about these aspects.
• It also describes the commands that are not reached from the GUI; i.e.,
simulation, the recursive algorithms and more advanced model structure
definitions.
3-2
The Toolbox Commands
The Toolbox Commands
It may be useful to recognize several layers of the System Identification
Toolbox. Initially concentrate on the first layer of basic tools, which contains
the commands from the System Identification Toolbox that any user must
master. You can proceed to the next levels whenever an interest or the need
from the applications warrants it. The layers are described in the following
paragraphs:
Layer 0: Help Functions. Help ident gives an overview of available commands.
idhelp gives access to a “micromanual” of command-line help, with several
subhelps like idhelp, evaluate, etc.
Layer 1: Basic Tools for Estimating Black-Box Models. The first layer contains the basic
tools for estimating models from measured data. It is necessary to know the
basics of the data representation and the simple commands to build and
evaluate black-box models. The commands are:
• For data representation: iddata, plot
• For nonparametric estimation of impulse and frequency response: impulse,
step, spa
• For estimating black-box models of state-space and input-output type: pem,
arx
• For evaluating models: compare, resid
• For displaying model characteristics: bode, nyquist, pzmap, step, view
• Looking at parametric model characteristics: By field referencing, like
Mod.A, Mod.dA, etc.
The corresponding background is given in the next few sections of this
“Tutorial.”
Layer 2: Creating Models for Simulation and Transforming Models. To define models, to
generate inputs and simulate models
idarx, idpoly, idss, idinput, sim
To transform models to other representations
arxdata, polydata, ssdata, tfdata, zpkdata
3-3
3
Tutorial
Layer 3: Model Structure Selection. The third layer of the toolbox contains some
useful techniques to select orders and delays.
arxstruc, selstruc
Layer 4: Structured Models and Further Model Conversions. The fourth layer contains
transformations between continuous and discrete time, and functions for
estimating completely general model structures for linear systems. The
commands are
c2d, d2c, idss, idgrey, pe, predict
ss, tf, zp, frd (to be used with the Control System Toolbox)
The corresponding material is covered in “Defining Model Structures” on
page 3-35 and in “Examining Models” on page 3-49.
Layer 5: Recursive Identification. Recursive (adaptive, online) methods of
parameter estimation are covered by the commands
rarmax, rarx, rbj, roe, rpem, rplr
They are covered in “Recursive Parameter Estimation” on page 3-78.
(See the beginning of the “Command Reference” chapter for a complete list of
available functions.)
3-4
An Introductory Example to Command Mode
An Introductory Example to Command Mode
A demonstration M-file called iddemo.m provides several examples of what
might be typical sessions with the System Identification Toolbox. To start the
demo, execute iddemo from inside MATLAB.
Before giving a formal treatment of the capabilities and possibilities of the
toolbox, this example is designed to get you started with the software quickly.
This example is essentially the same as demo #2 in iddemo. You may want to
invoke MATLAB at this time, execute the demo, and follow along.
Data have been collected from a laboratory scale process. (Feedback’s Process
Trainer PT326; see page 526 in Ljung, 1999.(For references, see “Reading More
About System Identification” on page 1-21 in this book.) The process operates
much like a common hand-held hair dryer. Air is blown through a tube after
being heated at the inlet to the tube. The input to the process is the power
applied to a mesh of resistor wires that constitutes the heating device. The
output of the process is the air temperature at the outlet, measured in volts by
a thermocouple sensor.
One thousand input-output data points were collected from the process as the
input was changed in a random fashion between two levels. The sampling
interval is 80 ms. The data were loaded into MATLAB in ASCII form and are
now stored as the vectors y2 (output) and u2 (input) in the file dryer2.mat.
First load the data.
load dryer2
It contains the input vector u2, the output vector y2. First form the data object.
dry = iddata(y2,u2,0.08);
To get information about the data, just type the name.
dry
To get an overview of all the information contained in the iddata object dry,
type
get(dry)
3-5
3
Tutorial
For better bookkeeping, give names to input and outputs.
dry.InputName = 'Power';
dry.OutputName = 'Temperature';
Select the 300 first values for building a model.
ze = dry(1:300);
Plot the interval from sample 200 to 300.
plot(ze(200:300)),
Remove the constant levels and make the data zero-mean.
ze = detrend(ze);
Let us first estimate the impulse response of the system by correlation analysis
to get some idea of time constants and the like.
impulse(ze,'sd',3)
This gives a plot with dash-dotted lines marking a confidence region
corresponding to three standard deviations (ca 99.9%). From this it is easy to
see if there is a time delay in the system.
The simplest way to get started, is to build a state-space model where the order
is automatically determined, using a prediction error method.
m1 = pem(ze)
When the calculations are finished, a display of the basic information about m1
is shown. Anytime m1 is typed, this display is shown. Typing present(m1) will
give some more information about the model, including uncertainties.
To retrieve the properties of this model we could, e.g., find the A matrix of the
state space representation by
A = m1.a
m1 is a model object, and
get(m1)
gives a list of all information stored in the model.
3-6
An Introductory Example to Command Mode
How good is this model? One way to find out is to simulate it and compare the
model output with measured output. We then select a portion of the original
data that was not used to build the model, e.g., from sample 800 to 900.
zv = dry(800:900);
zv = detrend(zv);
compare(zv,m1);
The Bode plot of the model is obtained by
bode(m1)
An alternative is to consider the Nyquist plot, and mark uncertainty regions at
certain frequencies with ellipses, corresponding to 3 (say) standard deviations:
nyquist(m1,'sd',3)
We can also compare the step response of the model with one that is directly
computed from data (ze) in a nonparametric way.
step(m1,ze)
To study a model with prescribed structure, we compute a difference equation
model with two poles, one zero, and three delays.
m2 = arx(ze,[2 2 3])
This gives a model of the form
y ( t ) + a 1 y ( t – T ) + a 2 y ( t – 2T ) = b 1 u ( t – 3T ) + b 2 u ( t – 4T )
where T is the sampling interval (here 0.08 seconds). This model, known as an
ARX model, tries to explain or compute the value of the output at time t, given
previous values of y and u. To compare its performance on validation data with
m1, type
compare(zv,m1,m2);
Compute and plot the poles and zeros of the models.
pzmap(m1,m2)
The uncertainties of the poles and zeros can also be plotted.
pzmap(m1,m2,'sd',3), % '3' denotes the number of standard
deviations
3-7
3
Tutorial
Estimate the frequency response by a nonparametric spectral analysis method.
gs = spa(ze);
Compare with the frequency functions from the parametric models.
bode(m1,m2,gs)
3-8
The System Identification Problem
The System Identification Problem
This section discusses different basic ways to describe linear dynamic systems
and also the most important methods for estimating such models.
Impulse Responses, Frequency Functions, and
Spectra
e
y
u
The basic input-output configuration is depicted in the figure above. Assuming
unit sampling interval, there is an input signal
u ( t );
t = 1, 2, …, N
and an output signal
y ( t );
t = 1, 2, … , N
Assuming the signals are related by a linear system, the relationship can be
written
y ( t ) = G ( q )u ( t ) + v ( t )
(3-1)
where q is the shift operator and G ( q )u ( t ) is short for
∞
G ( q )u ( t ) =
∑ g ( k )u ( t – k )
(3-2)
k=1
and
3-9
3
Tutorial
∞
∑ g ( k )q
G(q) =
–k
–1
q u(t) = u(t – 1 )
;
(3-3)
k=1
The numbers { g ( k ) } are called the impulse response of the system. Clearly,
g ( k ) is the output of the system at time k if the input is a single (im)pulse at
time zero. The function G ( q ) is called the transfer function of the system. This
iω
function evaluated on the unit circle ( q = e ) gives the frequency function
iω
G( e )
(3-4)
In (3-1) v ( t ) is an additional, unmeasurable disturbance (noise). Its properties
can be expressed in terms of its (power) spectrum
Φv ( ω )
(3-5)
which is defined by
∞
Φv ( ω ) =
∑
R v ( τ )e
– iωτ
(3-6)
τ = –∞
where R v ( τ ) is the covariance function of v ( t )
R v ( τ ) = Ev ( t )v ( t – τ )
(3-7)
and E denotes mathematical expectation. Alternatively, the disturbance v ( t )
can be described as filtered white noise
v ( t ) = H ( q )e ( t )
(3-8)
where e ( t ) is white noise with variance λ and
iω 2
Φv ( ω ) = λ H ( e )
(3-9)
Equations (3-1) and (3-8) together give a time domain description of the system
y ( t ) = G ( q )u ( t ) + H ( q )e ( t )
where G is the transfer function of the system. Equations (3-4) and (3-5)
constitute a frequency domain description.
3-10
(3-10)
The System Identification Problem
iω
G ( e );
Φv ( ω )
(3-11)
The impulse response (3-3) and the frequency domain description (3-11) are
called nonparametric model descriptions since they are not defined in terms of
a finite number of parameters. The basic description (3-10) also applies to the
multivariable case; i.e., to systems with several (say nu) input signals and
several (say ny) output signals. In that case G ( q ) is an ny-by-nu matrix while
H ( q ) and Φ v ( ω ) are ny-by-ny matrices.
Polynomial Representation of Transfer Functions
Rather than specifying the functions G and H in (3-10) in terms of functions of
–1
the frequency variable ω , you can describe them as rational functions of q
and specify the numerator and denominator coefficients in some way.
A commonly used parametric model is the ARX model that corresponds to
G(q) = q
– nk
B(q)
⋅ ------------ ;
A(q)
1
H ( q ) = -----------A(q)
(3-12)
–1
where B and A are polynomials in the delay operator q :
A ( q ) = 1 + a1 q
–1
B ( q ) = b 1 + b2 q
+ …… + a na q
–1
– na
+ …… + b nb q
– nb + 1
(3-13)
Here, the numbers na and nb are the orders of the respective polynomials. The
number nk is the number of delays from input to output. The model is usually
written
A ( q )y ( t ) = B ( q )u ( t – nk ) + e ( t )
(3-14)
or explicitly
y ( t ) + a 1 y ( t – 1 ) + …… + a na y ( t – na ) =
b 1 u ( t – nk ) + b 2 u ( t – nk – 1 ) + …… + b nb u ( t – nk – nb + 1 ) + e ( t )
(3-15)
Note that (3-14) - (3-15) apply also to the multivariable case, with ny output
channels and nu input channels. Then A ( q ) and the coefficients a i become
ny-by-ny matrices, B ( q ) and the coefficients b i become ny-by-nu matrices.
3-11
3
Tutorial
Another very common, and more general, model structure is the ARMAX
structure
A ( q )y ( t ) = B ( q )u ( t – nk ) + C ( q )e ( t )
(3-16)
Here, A ( q ) and B ( q ) are as in (3-13), while
C( q ) = 1 + c1q
–1
+ … + c nc q
– nc
An Output-Error (OE) structure is obtained as
B( q)
y ( t ) = ------------ u ( t – nk ) + e ( t )
F( q)
(3-17)
with
F ( q ) = 1 + f1 q
–1
+ … + fnf q
– nf
The so-called Box-Jenkins (BJ) model structure is given by
C(q )
B( q)
y ( t ) = ------------ u ( t – nk ) + ------------- e ( t )
D(q)
F(q)
(3-18)
with
D(q ) = 1 + d1q
–1
+ … + d nd q
– nd
All these models are special cases of the general parametric model structure.
B(q )
C( q)
A ( q )y ( t ) = ------------ u ( t – nk ) + ------------- e ( t )
F(q)
D(q)
(3-19)
The variance of the white noise { e ( t ) } is assumed to be λ .
Within the structure of (3-19), virtually all of the usual linear black-box model
structures are obtained as special cases. The ARX structure is obviously
obtained for nc = nd = nf = 0 . The ARMAX structure corresponds to
nf = nd = 0 . The ARARX structure (or the “generalized least squares
model”) is obtained for nc = nf = 0 , while the ARARMAX structure (or
“extended matrix model”) corresponds to nf = 0 . The Output-Error model is
obtained with na = nc = nd = 0 , while the Box-Jenkins model corresponds
to na = 0 . (See Section 4.2 in Ljung (1999) for a detailed discussion.)
3-12
The System Identification Problem
The same type of models can be defined for systems with an arbitrary number
of inputs. They have the form
·
B1 ( q )
B nu ( q )
C( q)
A ( q )y ( t ) = --------------- u 1 ( t – nk 1 ) + ...+ ------------------- u nu ( t – nk nu ) + ------------- e ( t )
F1 ( q )
F nu ( q )
D(q)
(3-20)
State-Space Representation of Transfer Functions
A common way of describing linear systems is to use the state-space form.
x ( t + 1 ) = Ax ( t ) + Bu ( t )
y ( t ) = Cx ( t ) + Du ( t ) + v ( t )
(3-21)
Here the relationship between the input u ( t ) and the output y ( t ) is defined
via the nx-dimensional state vector x ( t ) . In transfer function form (3-21)
corresponds to (3-1) with
–1
G ( q ) = C ( qI nx – A ) B + D
(3-22)
Here I nx is the nx by nx identity matrix. Clearly (3-21) can be viewed as one
way of parametrizing the transfer function: Via (3-22) G ( q ) becomes a function
of the elements of the matrices A, B, C, and D.
To further describe the character of the noise term v ( t ) in (3-21) a more flexible
innovations form of the state-space model can be used.
x ( t + 1 ) = Ax ( t ) + Bu ( t ) + Ke ( t )
y ( t ) = Cx ( t ) + Du ( t ) + e ( t )
(3-23)
This is equivalent to (3-10) with G ( q ) given by (3-22) and H ( q ) by
–1
H ( q ) = C ( qI nx – A ) K + I ny
(3-24)
Here ny is the dimension of y ( t ) and e ( t ) .
It is often possible to set up a system description directly in the innovations
form (3-23). In other cases, it might be preferable to describe first the nature of
disturbances that act on the system. That leads to a stochastic state-space
model
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x ( t + 1 ) = Ax ( t ) + Bu ( t ) + w ( t )
(3-25)
y ( t ) = Cx ( t ) + Du ( t ) + e ( t )
where w ( t ) and e ( t ) are stochastic processes with certain covariance
properties. In stationarity and from an input-output view, (3-25) is equivalent
to (3-23) if the matrix K is chosen as the steady-state Kalman gain. How to
compute K from (3-25) is described in the Control System Toolbox
documentation.
Continuous-Time State-Space Models
It is often easier to describe a system from physical modeling in terms of a
continuous-time model. The reason is that most physical laws are expressed in
continuous time as differential equations. Therefore, physical modeling
typically leads to state-space descriptions like
x· ( t ) = Fx ( t ) + Gu ( t )
(3-26)
y ( t ) = Hx ( t ) + Du ( t ) + v ( t )
Here, x· means the time derivative of x . If the input is piece-wise constant over
time intervals kT ≤ t < ( k + 1 )T , then the relationship between u [ k ] = u ( kT )
and y [ k ] = y ( kT ) can be exactly expressed by (3-21) by taking
T
A = e
FT
B =
;
∫e
Fτ
G dτ;
C = H
(3-27)
0
and associate y ( t ) with y [ t ] , etc. If you start with a continuous-time
innovations form
˜ e(t )
x· ( t ) = Fx ( t ) + Gu ( t ) + K
y ( t ) = Hx ( t ) + Du ( t ) + e ( t )
(3-28)
the discrete-time counterpart is given by (3-23) where still the relationships
˜ and K is somewhat more
(3-27) hold. The exact connection between K
complicated, though. An ad hoc solution is to use
T
K =
∫e
0
3-14
Fτ
˜ dτ;
K
(3-29)
The System Identification Problem
in analogy with G and B. This is a good approximation for short sampling
intervals T.
Estimating Impulse Responses
Consider the descriptions (3-1) and (3-2). To directly estimated the impulse
response coefficients, also in the multivariable case, it is suitable to define a
high order Finite Impulse Response (FIR) model.
y ( t ) = g ( 0 )u ( t ) + g ( 1 )u ( t – 1 ) + … + g ( n )u ( t – n )
(3-30)
and estimate the g-coefficients by the linear least squares method. In fact, to
check if there are non-causal effects from input to output, e.g., due to feedback
from y in the generation of u (closed loop data), g for negative lags can also be
estimated.
y ( t ) = g ( – m )u ( t + m ) + … + g ( – 1 )u ( t + 1 ) + g ( 0 )u ( t ) +
g ( 1 )u ( t – 1 ) + … + g ( n )u ( t – n )
(3-31)
If u is white noise, the impulse response coefficients will be correctly estimated,
even if the true dynamics from u to y is more complicated than these models.
Therefore it is natural to filter both the output and the input through a filter
that makes the input sequence as white as possible, before estimating the g.
This is the essence of correlation analysis for estimating impulse responses.
Estimating Spectra and Frequency Functions
This section describes methods that estimate the frequency functions and
spectra (3-11) directly. The cross-covariance function Ryu ( τ ) between y ( t ) and
u ( t ) is defined as Ey ( t + τ )u ( t ) analogously to (3-7). Its Fourier transform, the
cross spectrum, Φ yu ( ω ) is defined analogously to (3-6). Provided that the input
u ( t ) is independent of v ( t ) , the relationship (3-1) implies the following
relationships between the spectra.
iω 2
Φ y ( ω ) = G ( e ) Φ u ( ω ) + Φv ( ω )
iω
(3-32)
Φ yu ( ω ) = G ( e )Φ u ( ω )
By estimating the various spectra involved, the frequency function and the
disturbance spectrum can be estimated as follows.
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ˆ (τ) , R (τ) ,
Form estimates of the covariance functions (as defined in (3-7)) R
y
yu
ˆ
and R u ( τ ) , using
N
1
ˆ ( τ ) = ---R
yu
N
∑ y ( t + τ )u ( t )
(3-33)
t=1
and analog expressions for the others. Then, form estimates of the
corresponding spectra
M
ˆ
Φy ( ω ) =
∑
– iωτ
Rˆy ( τ )W M ( τ )e
(3-34)
τ = –M
and analogously for Φ u and Φ yu . Here W M ( τ ) is the so-called lag window and
M is the width of the lag window. The estimates are then formed as
ˆ
Φ yu ( ω )
ˆ ( e iω ) = ------------------G
;
N
ˆ
Φu ( ω )
2
ˆ
ˆ
ˆ
Φ yu ( ω )
Φ v ( ω ) = Φ y ( ω ) – -----------------------ˆ
Φu( ω )
(3-35)
This procedure is known as spectral analysis. (See Chapter 6 in Ljung (1999).)
Estimating Parametric Models
Given a description (3-10) and having observed the input-output data u, y, the
(prediction) errors e ( t ) in (3-10) can be computed as
–1
e ( t ) = H ( q ) [ y ( t ) – G ( q )u ( t ) ]
(3-36)
These errors are, for given data y and u, functions of G and H. These in turn
are parametrized by the polynomials in (3-14)-(3-19) or by entries in the
state-space matrices defined in (3-26)–(3-29). The most common parametric
identification method is to determine estimates of G and H by minimizing
N
V N ( G, H ) =
∑e
t=1
that is
3-16
2
(t)
(3-37)
The System Identification Problem
N
ˆ ,H
ˆ ] = argmin
[G
N
N
∑e
2
(t)
(3-38)
t=1
This is called a prediction error method. For Gaussian disturbances it coincides
with the maximum likelihood method. (See Chapter 7 in Ljung (1999).)
A somewhat different philosophy can be applied to the ARX model (3-14). By
forming filtered versions of the input
N ( q )s ( t ) = M ( q )u ( t )
(3-39)
and by multiplying (3-14) with s ( t – k ) , k = 1, 2, … , na and u ( t – nk + 1 – k ) ,
k = 1 , 2, … , nb and summing over t, the noise in (3-14) can be correlated out
and solved for the dynamics. This gives the instrumental variable method, and
s ( t ) are called the instruments. (See Section 7.6 in Ljung (1999).)
Subspace Methods for Estimating State-Space
Models
The state-space matrices A, B, C, D, and K in (3-23) can be estimated directly,
without first specifying any particular parametrization by efficient subspace
methods. The idea behind this can be explained as follows: If the sequence of
state vectors x(t) were known, together with y(t) and u(t), Eq. (3-23) would be a
linear regression, and C and D could be estimated by the least squares method.
Then e(t) could be determined, and treated as a known signal in (3-23), which
then would be another linear regression model for A, B and K. (One could also
treat (3-21) as a linear regression for A, B, C, and D with y(t) and x(t+1) as
simultaneous outputs, and find the joint process and measurement noises as
the residuals from this regression. The Kalman gain K could then be computed
from the Riccati equation.) Thus, once the states are known, the estimation of
the state-space matrices is easy.
How to find the states x(t)? All states in representations like (3-23) can be
formed as linear combinations of the k-step ahead predicted outputs
(k=1,2,...,n). It is thus a matter of finding these predictors, and then selecting
a basis among them. The subspace methods form an efficient and numerically
reliable way of determining the predictors by projections directly on the
observed data sequences. See Sections 7.3 and 10.6 in Ljung (1999). For more
details, see the references under n4sid in the “Command Reference” chapter.
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Data Representation and Nonparametric Model Estimation
This and the following sections give an introduction to the basic functions in
the System Identification Toolbox. Not all of the options available when using
the functions are described here; see the “Command Reference” chapter and
the online Help facility.
Data Representation
The observed output and input signals, y ( t ) and u ( t ) , are represented as
column vectors y and u. Row k corresponds to sample number k. For
multivariable systems, each input (output) component is represented as a
column vector, so that u becomes an N-by-nu matrix (N = number of sampled
observations, nu = number of input channels). The output-input data is
collectively represented in the iddata format. This is the basic object for
dealing with signals in the toolbox. It is used by most of the commands. It is
created by
Data = iddata(y,u,Ts)
where y is a column vector or an N-by-ny matrix. The columns of y correspond
to the different output channels. Similarly u is a column vector or an N-by-nu
matrix containing the signals of the input channels. Ts is the sampling
interval. This construction is sufficient for almost all purposes.
The data is then plotted by plot(Data) and portions of the data record are
selected as in
ze = Data(1:300)
The signals in the output channels are retrieved by Data.OutputData or for
short, Data.y. Similarly the input signals are obtained by Data.InputData or
Data.u.
For a time series (no input channels) use Data = iddata(y), or let u = [ ].
An iddata object can also contain just an input, by letting y = [ ].
The sampling interval can be changed by set(Data,'Ts',0.3) or, simpler, by
Data.Ts = 0.3
More details about the iddata object is given at the end of this section.
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Data Representation and Nonparametric Model Estimation
Correlation Analysis
The correlation analysis procedure described in “Estimating Impulse
Responses” on page 3-15 is implemented in the function impulse.
impulse(Data)
This function plots the estimated impulse response. Adding an argument 'sd'
as in
impulse(Data,'sd',3)
it also marks a confidence region corresponding to (in this case) three standard
deviations. The result can be stored and replotted.
ir = impulse(Data)
impulse(ir,'sd',3)
An alternative is the command step that plots the step response, calculated
from the impulse estimate.
step(Data)
Spectral Analysis
The function spa performs spectral analysis according to the procedure in
(3-35)–(3-37).
g = spa(Data)
Here Data contains the output-input data in the iddata object as above. g is
returned as an idfrd (Identified frequency domain) model object, that contains
the estimated
frequency function G N and the estimated disturbance
ˆ
spectrum Φ v in (3-37), as well as estimated uncertainty covariances. The idfrd
object is described in the “Command Reference” chapter, but for normal use you
do not have to bother about these details. The frequency function, or frequency
response, G in g can be graphed by the commands bode, ffplot, or nyquist.
The noise spectrum is retrieved by g('n') ('n' for “Noise”) so
g = spa(Data)
bode(g)
bode(g('n'))
performs the spectral analysis, and plots first G and then Φ v . bode gives
logarithmic amplitude and frequency scales (in rad/sec) and linear phase scale,
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while ffplot gives linear frequency scales (in Hz). The uncertainty of the
estimates is displayed by adding the argument 'sd' as in
bode(g,'sd',3)
which will display, by dash-dotted lines, a confidence region around the
estimate, that corresponds to (in this case) three standard deviations. Adding
an argument 'fill' will show the uncertainty region instead as a filled
region.
bode(g,'sd',3,'fill')
Similarly
nyquist(g)
gives a Nyquist plot of the frequency function, i.e. a plot of the real part versus
the imaginary part of G
If Data = y is a time series, that is Data has no input channel, spa returns an
estimate of the spectrum of that signal:
g= spa(y)
ffplot(g)
In the computations (3-35)-(3-37), spa uses as a lag window the Hamming
window for W ( τ ) with a default length M equal to the minimum of 30 and a
tenth of the number of data points. This window size M can be changed to an
arbitrary number by
g = spa(Data,M)
The rule is that as M increases, the estimated frequency functions show sharper
details, but are also more affected by random disturbances. A typical sequence
of commands that test different window sizes is
g10 = spa(Data,10)
g25 = spa(Data,25)
g50 = spa(Data,50)
bode(g10, g25, g50)
An empirical transfer function estimate is obtained as the ratio of the output
and input Fourier transforms with
g = etfe(Data)
3-20
Data Representation and Nonparametric Model Estimation
This can also be interpreted as the spectral analysis estimate for a window size
that is equal to the data length. For time series, etfe gives the periodogram as
a spectral estimate. The function also allows some smoothing of the crude
estimate; it can be a good alternative for signals and systems with sharp
resonances. See the “Command Reference” chapter for more information.
More on the Data Representation in iddata
Some Bookkeeping Facilities
The input and output channels are given default names like y1, y2, u1, u2, etc.
The channel names can be set by
set(Data,'InputName',{'Voltage','Current'},'OutputName','Tempera
ture')
(two inputs and one output is this example) and these names will then follow
the object and appear in all plots. The names will also be inherited by models
that are estimated from the data.
Similarly, channel units can be specified using the properties OutputUnit and
InputUnit. These units, when specified, will be used in plots.
The timepoints associated with the data samples are determined by the
sampling interval Ts and the time of the first sample, Tstart.
Data.Tstart = 24
The actual time point values are given by the property, SamplingInstants, as
in
plot(Data.sa,Data.u)
for a plot of the input with correct time points. Autofill is used for all properties,
and they are case insensitive. For easy writing 'u' is synonymous to 'Input'
and 'y' to 'Output' when referring to the properties.
Manipulating Channels
An easy way to set and retrieve channel properties is to use subscripting. The
subscripts are defined as
Data(samples,outputs,inputs),
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Tutorial
so Dat(:,3,:) is the data object obtained from Dat by keeping all input
channels, but only output channel 3. (Trailing ’:’s can be omitted so
Dat(:,3,:)= Dat(:,3).)
The channels can also be retrieved by their names, so that
Dat(:,{'speed','flow'},[ ])
is the data object where the indicated output channels have been selected and
no input channels are selected.
Moreover
Dat1(101:200,[3 4],[1 3]) = Dat2(1001:1100,[1 2],[6 7])
will change samples 101 to 200 of output channels 3 and 4 and input channels
1 and 3 in the iddata object Dat1 to the indicated values from iddata object
Dat2. The names and units of these channels will then also be changed
accordingly.
To add new channels, use horizontal concatenation of iddata objects
Dat =[Dat1, Dat2];
(see “Adding Channels” on page 3-24) or add the data record directly, so that
Dat.u(:,5) = u
will add a fifth input to Dat.
Nonequal Sampling
The property SamplingInstants gives the sampling instants of the data points.
It can always be retrieved by get(Dat,'SamplingInstants') (or Dat.s) and is
then computed from Dat.Ts and Dat.Tstart. SamplingInstants can also be
set to an arbitrary vector of the same length as the data, so that nonequal
sampling can be handled. Ts is then automatically set to [ ]. Most of the
estimation routines, though, do not handle unequally sampled data.
Multiple Experiments
The iddata object can also store data from separate experiments. The property
ExperimentName is used to separate the experiments. The number of data as
well as the sampling properties can vary from experiment to experiment, but
the input and output channels must be the same. (Use NaN to fill unmeasured
3-22
Data Representation and Nonparametric Model Estimation
channels in certain experiments.) The data records will be cell arrays, where
the cells contain data from each experiment.
Multiple experiments can be defined directly by letting the 'y' and 'u'
properties as well as 'Ts' and 'Tstart' be cell arrays.
It is normally easier to create multi-experiment data by merging experiments
as in
Dat = merge(Dat1,Dat2)
See merge (iddata) in the “Command Reference.” Storing multiple
experiments as one iddata object may be very useful to handle experimental
data that has been collected on different occasions, or when a data set has been
split up to remove “bad” portions of the data. All the toolbox’s routines accept
multiple experiment data.
Experiments can be retrieved by the command getexp, as in getexp(Dat,3) or
getexp(Dat,’Period1’). They can also be set and retrieved by subscripting
with a fourth index: Dat(:,:,:,3)} is experiment number 3 and
Dat(:,:,:,{'Day1','Day4'}) retrieves the two experiments with the
indicated names.
The subscripting can be combined: Dat(1:100,[2,3],[4:8],3) gives the 100
first samples of output channels 2 and 3 and input channels 4 to 8 of
experiment number 3. It can also be used for subassignment
Dat(:,:,:,’'Run4') = Dat2
adds the data in Dat2 as a new experiment with name 'Run4'. See iddemo # 8
for an illustration of how multiple experiments can be used.
iddata Properties
Type get(Dat) or see iddata in the “Command Reference” for a complete list
of iddata properties.
Subreferencing
The samples, outputs and input channels can be referenced according to
Data(samples,outputs,inputs)
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Use colon (:) to denote all samples/channels and the empty matrix ([ ]) to denote
no samples/channels. The channels can be referenced by number or by name.
For several names a cell array must be used.
Dat2 = Dat(:,'y3',{'u1','u4'})
Dat2 = Dat(:,3,[1 4])
Logical expressions will also work.
Dat3 = Dat2(Dat2.sa>1.27&Dat2.sa<9.3)
will select the samples with time marks between 1.27 and 9.3.
Subreferencing with curly brackets refers to the experiment.
Data{Experiment}(samples,outputs,inputs)
Any subreferenced variable can also be assigned.
Data{'Exp3'}.y = flow(1:700,:)
Data(1:10,1,1) = Dat1(101:110,2,3)
Adding Channels
Dat = [Dat1,Dat2,...,DatN]
creates an iddata object Dat, consisting of the input and output channels in
Dat1,... DatN. Default channel names ('u1', 'u2', 'y1' , 'y2' etc) will
be changed so that overlaps in names are avoided, and the new channels will
be added.
If Datk contains channels with user-specified names that are already present
in the channels of Datj, j<k, these new channels will be ignored.
Adding Samples
Dat = [Dat1;Dat2;... ;DatN]
creates an iddata object Dat whose signals are obtained by stacking those of
Datk on top of each other. That is.
Dat.y = [Dat1.y;Dat2.y; ... DatN.y]
and similarly for the inputs. The Datk objects must all have the same number
of channels and experiments.
3-24
Parametric Model Estimation
Parametric Model Estimation
The System Identification Toolbox contains several functions for parametric
model estimation. They all share the same command structure.
m = function(Data,modstruc)
m =
...
function(Data,modstruc,'Property1',Value1,...'PropertyN',ValueN)
The argument Data is an iddata object that contains the output and input data
sequences, while modstruc specifies the particular structure of the model to be
estimated. The resulting estimated model is contained in m. It is a model object
that stores various information. The model objects will be described in
“Defining Model Structures” on page 3-35, but for most use of the toolbox, you
do not have to consider the details of these objects. Just typing the model name
m
will give a concise display of the model. The command
present(m)
gives some more details, while
get(m)
gives a complete list of the model’s properties. The property values can be
easily retrieved just by dot-referencing; for example,
m.par
retrieves the estimated parameters.
In the function call (...,'Property1', Value1,...,'PropertyN',ValueN) is
a list of properties that can be assigned to affect the model structure, as well as
the estimation algorithm. A list of typical properties is given at the end of this
section. The model m is also immediately prepared for displaying and analyzing
its characteristics as well as for transforming it to other representations, as in
bode(m)
compare(Data,m)
[A,B,C,D, K] = ssdata(m)
See “Examining Models” on page 3-49 for a detailed discussion of these
possibilities.
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In the following, Data denotes an iddata object that contains the input output
data as described in the previous section. It may also just contain an output
signal, i.e., a time series.
ARX Models
To estimate the parameters a i and b i of the ARX model (3-14), use the
function arx.
m
= arx(Data,[na nb nk])
Here na, nb, and nk are the corresponding orders and delays in (3-15) on page
3-11 that define the exact model structure. The function arx implements the
least squares estimation method, using QR-factorization for overdetermined
linear equations.
An alternative is to use the Instrumental Variable (IV) method described in
connection with (3-39). This is obtained with
m = iv4(Data,[na nb nk])
which gives an automatic (and approximately optimal) choice of the filters N
and M in (3-39). (See the procedure (15.21)-(15.26) in Ljung (1999).)
Both arx and iv4 are applicable to arbitrary multivariable systems. If you have
ny outputs and nu inputs, the orders are defined accordingly: na is an ny-by-ny
matrix whose i -j entry gives the order of the polynomial that relates past
values of y j to the current value of y i (i.e., past values of y j up to
y j ( t – na ( i, j ) ) are used when predicting y i ( t ) ) . Similarly, the i -j entries of the
ny-by-nu matrices nu and nk, respectively, give the order and delay from input
number j when predicting output number i. (See “Multivariable ARX Models:
The idarx Model” on page 3-37 and the“Command Reference” chapter for exact
details.)
AR Models
For a single output signal y ( t ) the counterpart of the ARX model is the AR
model.
A ( q )y ( t ) = e ( t )
The arx command also covers this special case
m = arx(y,na)
3-26
(3-40)
Parametric Model Estimation
but for scalar signals more options are offered by the command
m = ar(y,na)
which has an option that allows you to choose the algorithm from a group of
several popular techniques for computing the least squares AR model. Among
these are Burg’s method, a geometric lattice method, the Yule-Walker
approach, and a modified covariance method. (See the “Command Reference”
chapter for details.) The counterpart of the iv4 command is
m = ivar(y,na)
which uses an instrumental variable technique to compute the AR part of a
time series.
General Polynomial Black-Box Models
Based on the prediction error method (3-38), you can construct models of
basically any structure. For the general model (3-19), there is the function
m = pem(Data,nn)
where nn gives all the orders and delays.
nn = [na nb nc nd nf nk]
The nonzero orders of the model can also be defined as property name/property
value pairs as in
m = pem(Data,'na',na,'nb',nb,'nc',nc,'nk',nk)
The input parameters are defined on page 3-12. The pem command covers all
cases of black-box linear system models. For the common special cases
m = armax(Data,[na nb nc nk])
m = oe(Data,[nb nf nk])
m = bj(Data,[nb nc nd nf nk])
can be used. These handle the model structures (3-16), (3-17) and (3-18),
respectively.
All the routines also cover single-output, multi-input systems of the type
B1 ( q )
B nu ( q )
C( q)
A ( q )y ( t ) = --------------- u 1 ( t – nk 1 ) + … + ------------------- u nu ( t – nk nu ) + ------------- e ( t ) (3-41)
D(q)
F1 ( q )
F nu ( q )
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where nb, nf, and nk are row vectors of the same lengths as the number of input
channels containing each of the orders and delays
nb = [nb1 ...
nf = [nf1 ...
nk = [nk1 ...
nbnu];
nfnu];
nknu];
These parameter estimation routines require an iterative search for the
minimum of the function (3-39). This search uses a special start-up procedure
based on least squares and instrumental variables (the details are given as
Equation (10.79) in Ljung (1999)). From the initial estimate, a Gauss-Newton
minimization procedure is carried out until the norm of the Gauss-Newton
direction is less than a certain tolerance. See Ljung (1999), Section 11.2 or
Dennis and Schnabel(1983) for details. See also the entry at the end of this
section on optional variables associated with the search.
The estimation routines also return the estimated covariance matrix of the
estimated parameter vector as part of m. This reflects the reliability of the
estimates. The covariance matrix estimate is computed under the assumption
that it is possible to obtain a “true” description in the given structure.
The routines pem, armax, oe, and bj can also be started at any initial value mi
that is a model object by replacing nn by mi. For example,
m = pem(Data,mi)
While the search is typically initialized using the built-in start-up procedure
giving just orders and delays (as described above), the ability to force a specific
initial condition is useful in several contexts. Some examples are mentioned in
“Initial Parameter Values” on page 3-90.
Information about how the minimization progresses can be supplied to the
MATLAB command window by the property trace. See the list at the end of
this section.
State-Space Models
Black-Box, Discrete Time Parametrizations
Suppose first that there is no particular knowledge about the internal
structure of the discrete-time state-space model (3-15). Any linear model is
sought. A simple approach then is to use
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Parametric Model Estimation
m = pem(Data)
This estimates a state-space model of an order (among 1 to 10) that seems
reasonable.
To find a black-box model of a certain order n, use
m = pem(Data,n)
To get a plot, from which the order can be determined among a list of orders
nn = [n1,n2,...,nN], use
m = pem(Data,'nx',nn)
All these black-box models are initialized by the subspace method n4sid. To
obtain the estimate from this routine, use
m = n4sid(Data,n)
Arbitrarily Structured Models in Discrete and Continuous Time
For state-space models of given structure, most of the effort involved relates to
defining and manipulating the structure. This is discussed in “Structured
State-Space Models with Free Parameters: the idss Model” on page 3-42 and
onwards. Once the structure is defined as ms, you can estimate its parameters
with
m = pem(Data,ms)
When the systems are multi-output, the following criterion is used for the
minimization,
N
det
∑ e ( t )e
T
(t)
(3-42)
t=1
which is the maximum likelihood criterion for Gaussian noise with unknown
covariance matrix.
The numerical minimization of the prediction error criterion (3-39) or (3-42)
can be a difficult problem for general model parametrizations. The criterion, as
a function of the free parameters, can define a complicated surface with many
local minima, narrow valleys, and so on. This may require substantial
interaction from the user, in providing reasonable initial parameter values,
and also by freezing certain parameter values (using the property
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FixedParameters) while allowing others to be free. Note that pem easily allows
the freezing of any parameters to their current/nominal values. The model
structure can also be directly manipulated as described in “Structured
State-Space Models with Free Parameters: the idss Model” on page 3-42. A
procedure that is often used for state-space models is to allow the noise
parameter in the K matrix free only when a reasonable model of the dynamic
part has been obtained.
Optional Variables
The estimation functions accept a list of property name/property value pairs
that may affect both the model structure and the estimation algorithm. For
complete lists of these properties, see algorithm properties, idarx, idmodel,
idpoly, idss, and idgrey in the “Command Reference” chapter. Some of them
are listed here. Note that any property, as well as values that are strings, can
be entered as any unambiguous, case-insensitive abbreviation, as is
m = pem(Data,mi,'fo','si').
Note 1 Algorithm is a property of idmodel. Any algorithm property can be
separately set as above. Also, if you have a standard algorithm set up that you
prefer, you can set those properties simultaneously as in
m = pem(Data,mi,'alg',myalg)
Note 2 The algorithm properties, like all other model properties, will be
inherited by the resulting model m. If you continue the estimation using m as
initial model, all previously set algorithm features will thus apply, unless you
specify otherwise.
Applying to All Estimation Methods
The following properties apply to all estimation methods:
• Focus
• MaxSize
• FixedParameter
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Parametric Model Estimation
Focus: This property affects the weighting applied to the fit between the model
and the data. It can be used to assure that the model approximates the true
system well over certain frequency intervals. Focus can assume the following
values:
• Prediction: This is the default and means that the model is determined by
minimizing the prediction errors. It corresponds to a frequency weighting
that is given by the input spectrum times the inverse noise model. Typically,
this favors a good fit at high frequencies. From a statistical variance point of
view, this is the optimal weighting, but then the approximation aspects
(bias) of the fit are neglected.
• Simulation: This means that frequency weighting of the transfer function fit
is given by the input spectrum. Frequency ranges where the input has
considerable power will thus be better described by the model. In other
words, the model approximation is such that the model will produce as good
simulations as possible, when applied to inputs with the same spectra as
used for the estimation. For models that have no disturbance model (A=C=D
for idpoly models and K=0 for idss models) there is no difference between
the Simulation and Prediction values. For models with a disturbance
description, this is estimated by a prediction error method, keeping the
estimated transfer function from input to output fixed. The resulting model
is guaranteed to be stable.
• Stability: The algorithm is modified so that a stable model is guaranteed, but
the weighing still correspond to prediction.
• Any SISO linear filter. Then the transfer function from input to output is
determined by a frequency fit with this filter times the input spectrum as
weighting function. The noise model is determined by a prediction error
method, keeping the transfer function estimate fixed. To obtain a good model
fit over a special frequency range, the filter should thus be chosen with a
passband over this range. For a model with no disturbance model, the result
is the same as first applying prefiltering to data using idfilt. The filter can
be specified in a few different ways:
- as any single-input-single-output idmodel
- as a ss, tf, or zpk model from the Control System Toolbox
- as {A,B,C,D} with the state-space matrices for the filter
- as {numerator, denominator} with the transfer function numerator/
denominator of the filter
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MaxSize: No matrix with more than MaxSize elements is formed by the
algorithm. Instead, for loops will be used. MaxSize will thus decide the
memory/speed trade-off, and can prevent slow use of virtual memory. MaxSize
can be any positive integer, but it is of course required that the input-output
data themselves contain less than MaxSize elements. The default value of
MaxSize is Auto, which means that the value is determined in the M-file
idmsize. This file may be edited by the user to optimize speed on a particular
computer. See also “Memory - Speed Trade-Offs” on page 3-89.
FixedParameter: A list of parameters that will be kept fixed to the nominal/
initial values and not estimated. This is a vector of integers containing the
indices of the fixed parameters, or a cell array of parameter names. If names
are used, wildcard entries apply, which may be convenient if you have groups
of parameters in your model. See the reference page of Algorithm properties.
Algorithm Properties That Apply to n4sid, Estimating State-Space Models
The properties that apply to sub-space model estimation are:
• N4Weight
• N4Horizon
These properties then also apply to pem for estimating black-box state-space
models, since pem is then initialized by the n4sid estimate
N4Weight: This property determines some weighting matrices used in the
singular-value decomposition that is a central step in the algorithm. Two
choices are offered: moesp that corresponds to the MOESP algorithm by
Verhaegen and cva which is the canonical variable algorithm by Larimore. The
default value is N4Weight = Auto, which gives an automatic choice between the
two options.
N4Horison: Determines the prediction horizons forward and backward, used by
the algorithm. This is a row vector with three elements: N4Horison =[r sy
su], where r is the maximum forward prediction horizon, i.e., the algorithms
uses up to r-step ahead predictors. sy is the number of past outputs, and su is
the number of past inputs that are used for the predictions. See Ljung (1999),
pages 345-348. These numbers may have a substantial influence of the quality
of the resulting model, and there are no simple rules for choosing them. Making
N4Horizon a k-by-3 matrix means that each row of N4Horison will be tried out,
and the value that gives the best (prediction) fit to data will be selected. If you
specify only one column in N4Horizon, the interpretation is r=sy=su. The
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Parametric Model Estimation
default choice is
N4Horizon = Auto, which uses the Akaike Information Criterion (AIC) for the
selection of sy and su. See the reference page for n4sid for literature
references.
Properties That Apply to Estimation Methods Using Iterative Search for
Minimizing a Criterion
The properties that govern the iterative search are:
• Trace
• LimitError
• MaxIter
• Tolerance
• SearchDirection
• Advanced
These properties apply to armax, bj, oe, and pem
Trace: This property determines the information about the iterative search
that is provided to the MATLAB command window:
• Trace = Off: No information is written to the screen
• Trace = On: Information about criterion values and the search process is
given for each iteration.
• Trace= Full: The current parameter values and the search direction are also
given (except in the “free” SSParameterization case for idss models)
LimitError: This variable determines how the criterion is modified from
quadratic to one that gives linear weight to large errors. Errors larger than
LimitError times the estimated standard deviation will carry a linear weight
in the criteria. The default value of LimitError is 1.6. LimitError =0 disables
the robustification and leads to a purely quadratic criterion. The standard
deviation is estimated robustly as the median of the absolute deviations from
the median, divided by 0.7. (See Eq. (15.9)-(15.10) in Ljung (1999).)
MaxIter: The maximum number of iterations performed during the search for
minimum. The iterations will stop when MaxIter is reached, or some other
stopping criterion is satisfied. The default value of MaxIter is 20. Setting
MaxIter=0 will return the result of the start-up procedure. The actual number
of used iterations is given by the property EstimationInfo.Iterations.
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Tolerance: Based on the Gauss-Newton vector computed at the current
parameter value, an estimate is made of the expected improvement of the
criterion at the next iteration. When this expected improvement is less than
Tolerance %, the iterations are stopped. Default value: 0.01.
SearchDirection: The direction along which a line search is performed to find
a lower value of the criterion function. It may assume the following values:
• gn: The Gauss-Newton direction (inverse of the Hessian times the gradient
direction) If no improvement is found along this direction, the gradient
direction is also tried out.
• gns: A regularized version of the Gauss-Newton direction. Eigenvalues less
than pinvtol of the Hessian are neglected, and the Gauss-Newton direction
is computed in the remaining subspace. (pinvtol is part of the ’advanced’
field: See Algorithm Properties in the “Command Reference” Chapter.
• lm: The Levenberg-Marquard method is used. This means that the next
parameter value is -pinv(H+d*I)*grad from the previous one, where H is the
Hessian, I is the identity matrix, grad is the gradient. d is a number that is
increased until a lower value of the criterion is found.
• Auto: A choice between the above is made in the algorithm. This is the
default choice.
One property of the returned model is EstimationInfo. That is a structure that
contains useful information about the estimation process. See EstimationInfo
in the “Command Reference” chapter for a list of fields.
Another important option is InitialState. See “Initial State” on page 3-91.
For the spectral analysis estimate, you can compute the frequency functions at
arbitrary frequencies. If the frequencies are specified in a row vector w, then
g = spa(z,M,w)
results in g being computed at these frequencies. You can generate
logarithmically spaced frequencies using the MATLAB logspace function. For
example
w = logspace(-3,pi,128)
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Defining Model Structures
Defining Model Structures
Since the System Identification Toolbox handles a wide variety of different
model structures, it is important that these can be defined in a flexible way. In
the previous section you saw how models are automatically produced in the
right form by the various estimation routines arx, iv4, oe, bj, armax, and pem,
if you just specify model orders and delays.
This section describes how model structures and models can be directly
defined. This may be required, for example, when creating a model for
simulation. Also, it many be necessary to define model structures that are not
of black-box type, but contain more detailed internal structure, reflecting some
physical insights into how the system works.
The general way of representing models and model structures in the System
Identification Toolbox is by various model objects. This section introduces the
commands (apart from the parametric estimation functions themselves) that
create these different models.
The model objects will contain a number of properties. For any model you can
type
get(m)
to see a list of the model’s properties, and
set(m)
to see what the assignable values are. See get and/or set in the “Command
Reference” chapter. Each property value can easily also be retrieved by
subreferencing as in
m.A
and set as in
m.b(3) = 27
See the “Command Reference” chapter for complete property lists. Here only
examples are given. Note that it is sufficient to use any case insensitive,
unambiguous abbreviation of the property names.
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Polynomial Black-Box Models: The idpoly Model
The general input-output form (3-19)
C( q)
B(q )
A ( q )y ( t ) = ------------ u ( t – nk ) + ------------- e ( t )
D(q)
F(q)
(3-43)
is defined by the five polynomials A(q), B(q), C(q), D(q), and F(q). These are
represented in the standard MATLAB format for polynomials. Polynomial
coefficients are stored as row vectors ordered by descending powers. For
example, the polynomial
A ( q ) = 1 + a1 q
–1
+ a2 q
–2
+ … + an q
–n
is represented as
A = [1 a1 a2 ...
an]
Delays in the system are indicated by leading zeros in the B ( q ) polynomial. For
example, the ARX model
y ( t ) – 1.5y ( t – 1 ) + 0.7y ( t – 2 ) = 2.5u ( t – 2 ) + 0.9u ( t – 3 )
(3-44)
is represented by the polynomials
A = [1 -1.5 0.7]
B = [0 0 2.5 0.9]
The idpoly representation of (3-43) is now created by the command
m = idpoly(A,B,C,D,F,lam,T)
lam is here the variance of the white noise source e ( t ) and T is the sampling
interval. Trailing arguments can be omitted for default values. The system
(3-44) can for example be represented by
m = idpoly([1 -1.5 0.7],[0 0 2.5 0.9])
In the multi-input case (3-41) B and F are matrices, whose row number k
corresponds to the k-th input. The command idpoly can also be used to define
time-continuous systems. See Chapter 4, “Command Reference” for details.
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Defining Model Structures
When m is defined, the polynomials and their orders can be easily retrieved and
changed, as in
m.a % for the A-polynomial
roots(m.a)
m.a(3)=0.95
Multivariable ARX Models: The idarx Model
A multivariable ARX model with nu inputs and ny outputs is given by
A ( q )y ( t ) = B ( q )u ( t ) + e ( t )
(3-45)
Here A(q) is an ny-by-ny matrix whose entries are polynomials in the delay
operator q-1. You can represent it as
A ( q ) = I ny + A 1 q
–1
+ … + A na q
– na
(3-46)
as well as the matrix
a 11 ( q ) a 12 ( q ) … a 1ny ( q )
A( q) =
a 21 ( q ) a 22 ( q ) … a 2ny ( q )
(3-47)
…
…
…
…
a ny1 ( q ) a ny 2 ( q ) … a nyny ( q )
where the entries a kj are polynomials in the delay operator q
1
a kj ( q ) = δ kj + a kj q
–1
na k j – na kj
+ … + a kj q
–1
:
(3-48)
This polynomial describes how old values of output number j affect output
number k. Here δ kj is the Kronecker-delta; it equals 1 when k = j , otherwise,
it is 0. Similarly, B ( q ) is an ny-by-nu matrix
B( q ) = B0 + B1 q
–1
+ …B nb q
– nb
(3-49)
or
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b 11 ( q ) b 12 ( q ) … b 1nu ( q )
B(q ) =
b 21 ( q ) b 22 ( q ) … b 2nu ( q )
…
…
…
…
b ny1 ( q ) b ny2 ( q ) … b nynu ( q )
(3-50)
with
1
b kj ( q ) = b kj q
– nk kj
nb kj – nk kj – nb kj + 1
+ … + b kj q
The delay from input number j to output number k is nk kj . To link with the
structure definition in terms of na, nb, nk in the arx and iv4 commands, note
that na is a matrix whose kj-element is nakj , while the kj-elements of nb and
nk are nb kj and nk kj respectively.
The idarx representation of the model (3-45) can be created by
m = idarx(A,B)
where A and B are 3-D arrays of dimensions ny-by-ny-by-(na+1) and
ny-by-nu-by-(nb+1), respectively, that define the matrix polynomials (3-46)
and (3-49).
A(:,:,k+1) = Ak
B(:,:,k+1) = Bk
Note that A(:,:,1) is always the identity matrix, and that leading zero
coefficients in B matrices define the delays.
Consider the following system with two outputs and three inputs.
y 1 ( t ) – 1.5y 1 ( t – 1 ) + 0.4y 2 ( t – 1 ) + 0.7y 1 ( t – 2 ) =
0.2u 1 ( t – 4 ) + 0.3u 1 ( t – 5 ) + 0.4u 2 ( t ) – 0.1u 2 ( t – 1 ) + 0.15u 2 ( t – 2 ) + e 1 ( t )
y 2 ( t ) – 0.2y 1 ( t – 1 ) – 0.7y 2 ( t – 2 ) + 0.01y1 ( t – 2 ) =
u 1 ( t ) + 2u 2 ( t – 4 ) + 3u 3 ( t – 1 ) + 4u 3 ( t – 2 ) + e 2 ( t )
which in matrix notation can be written as
3-38
Defining Model Structures
y ( t ) + – 1.5 0.4 y ( t – 1 ) + 0.7 0 y ( t – 2 ) = 0 0.4 0 u ( t ) +
– 0.2 0
0.01 – 0.7
1 0 0
0 – 0.1 0 u ( t – 1 ) + 0 0.15 0 u ( t – 2 ) + 0 0 0 u ( t – 3 ) +
0 0 3
0 0 4
00 0
0.2 0 0 u ( t – 4 ) + 0.3 0 0 u ( t – 5 )
0 2 0
0 0 0
This system is defined and simulated for a certain input u, and then
estimated in the correct ARX structure by the following string of commands.
A(:,:,1) = eye(2);
A(:,:,2) = [-1.5 0.4; -0.2 0];
A(:,:,3) = [0.7 0 ; 0.01 -0.7];
B(:,:,1) = [0 0.4 0;1 0 0];
B(:,:,2) = [0 -0.1 0;0 0 3];
B(:,:,3) = [0 0.15 0;0 0 4];
B(:,:,4) = [0 0 0;0 0 0];
B(:,:,5) = [0.2 0 0;0 2 0];
B(:,:,6) = [0.3 0 0;0 0 0];
m0 = idarx(A,B);
u = iddata([], idinput([200,3]));
e = iddata([], randn(200,2));
y = sim(m0, [u e]);
na = [2 1;2 2];
nb = [2 3 0;1 1 2];
nk = [4 0 0;0 4 1];
me = arx([y u],[na nb nk])
me.a % The estimated A-polynominal
Black-Box State-Space Models: the idss Model
The basic state-space models are the following ones: (See also “State-Space
Models” on page 3-28.).
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Discrete-Time Innovations Form
x ( kT + T ) = Ax ( kT ) + Bu ( kT ) + Ke ( kT )
y ( kT ) = Cx ( kT ) + Du ( kT ) + e ( kT )
(a)
(b)
x ( 0 ) = x0
(3-51)
( c)
Here T is the sampling interval, u ( kT ) is the input at time instant kT , and
y ( kT ) is the output at time kT . (See Ljung (1999) page 99.)
System Dynamics Expressed in Continuous Time
˜ w(t)
x· ( t ) = Fx ( t ) + Gu ( t ) + K
y ( t ) = Hx ( t ) + Du ( t ) + w ( t )
(3-52)
x ( 0 ) = x0
(See Ljung (1999), page 93.) It is often easier to define a parameterized
state-space model in continuous time because physical laws are most often
described in terms of differential equations. The matrices F, G, H, and D
contain elements with physical significance (for example, material constants).
The numerical values of these may or may not be known. To estimate unknown
parameters based on sampled data (assuming T is the sampling interval) first
transform (3-52) to (3-51) using the formulas (3-27). The value of the Kalman
~
gain matrix K in (3-51) or K in (3-52) depends on the assumed character of the
additive noises w ( t ) and e ( t ) in (3-25), and its continuous-time counterpart.
~
Disregard that link and view K in (3-51) (or K in (3-52)) as the basic tool to
model the disturbance properties. This gives the directly parametrized
innovations form. (See Ljung (1999) page 99.) If the internal noise structure is
important, you could use user-defined greybox structures (the idgrey object) as
in the example on page 3-47.
The discrete time model (3-51) can be put into the idss model by
m = idss(A,B,C,D,K,X0,'Ts',T)
and for the continuous-time model (3-52) use
m = idss(F,G,H,D,Kt,X0,'Ts',0)
Setting the sampling interval Ts to zero means a continuous-time model. The
model m can now be used for simulation and it can be examined by the various
commands. The parameterization of the matrices is by default “free” that is,
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Defining Model Structures
any elements in the matrices are freely adjustable by the estimation routines.
The parameters will be adjusted to data by
me = pem(Data,m)
The iterative search for the best fit is then initialized in the nominal matrices
A, B, C, D, K, X0. Note that the command me = pem(Data,4), which just defines
the model order, first estimates (using n4sid) a starting model m, from which
the search is initialized.
In this free parameterization, you can decide how to deal with the disturbance
model matrix K. Letting
m.DisturbanceModel = 'None'
(rather than ’Estimate') fixes the K-matrix to zero, thereby creating an
Output-Error model.
Letting
m.InitialState ='zero'
(rather than ’Estimate') sets the initial state vector x0 to zero.
The property nk determines the delays from the different inputs just as for
idpoly models. Thus
m.nk = [0,0,...,0]
(no delays) means that all elements of the D-matrix should be estimated, while
m.nk = [1,1,..,1]
fixes the D-matrix to zero.
With the parameterization of A, B, and C being completely free, a basis for the
state-space realization is automatically selected to give well-conditioned
calculations. An alternative is to specify an observer canonical form for A, B, C
by
m.sspar = 'Canonical'
(rather than 'Free'). This is still a black-box model, since the canonical form
covers all models of a certain order. The structure modifications can all be
combined at the estimation call
me = pem(Data,m,'sspar','can','dist','none','ini','z')
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which is the same as
set(m,'sspar','can','dist','none','ini','z')
me = pem(Data,m);
Structured State-Space Models with Free
Parameters: the idss Model
The System Identification Toolbox allows you to define arbitrary
parameterizations of the matrices in (3-51) or (3-52). To define the structure,
so called structure matrices are used. These are “shadow matrices” to A, B,
C, D, K, and X0, and have the same sizes and coincide with these at all matrix
elements that are known. The structure matrices are denoted by As, Bs, Cs, Ds,
Ks, and X0s and have the entry NaN at those elements that correspond to
unknown parameters to be estimated.
For example,
m.As = [NaN 0;0 NaN]
sets the structure matrix for A, called As, to a diagonal matrix, where the
diagonal elements are freely adjustable. Defining
m.A = [2 0; 0 3]
sets the nominal/initial values of these diagonal elements to 2 and 3,
respectively.
Example 3.1: A Discrete-Time Structure. Consider the discrete-time model
x( t + 1 ) =
1 θ1
0 1
x( t) +
θ2
θ3
u( t) +
θ4
θ5
e(t)
y( t) = 1 0 x( t) + e( t)
x(0) = 0
0
with five unknown parameters θ i , i=1,...,5. Suppose the nominal/initial values
of these parameters are -1, 2, 3, 4 and 5. This structure is then defined by
3-42
Defining Model Structures
m = idss([1, -1;0, 1],[2;3],[1,0],0,[4;5])
m.As = [1, NaN; 0 ,1];
m.Bs = [NaN;NaN];
m.Cs = [1, 0];
m.Ds = 0;
m.Ks = [NaN;NaN];
m.x0s = [0;0];
The definition thus follows in two steps. First the nominal model is defined.
Then the structure (known and unknown parameter values) is defined by the
structure matrices, As, Bs, etc.
Example 3.2: A Continuous-Time Model Structure. Consider the following model
structure
0 1
0
x· ( t ) =
x(t) +
u(t)
0 θ1
θ2
y( t) = 1 0 x( t) + e(t )
01
x( 0) =
θ3
0
This corresponds to an electrical motor, where y 1 ( t ) = x 1 ( t ) is the angular
position of the motor shaft and y 2 ( t ) = x 2 ( t ) is the angular velocity. The
parameter – θ 1 is the inverse time constant of the motor and – θ 2 ⁄ θ 1 is the
static gain from the input to the angular velocity. (See page Example 4.1 in
Ljung (1999).) The motor is at rest at time 0 but at an unknown angular
position. Suppose that θ 1 is around -1 and θ 2 is around 0.25. If you also know
that the variance of the errors in the position measurement is 0.01 and in the
angular velocity measurements is 0.1, you can then define an idss model using
m = idss([0 1;0
-1],[0;0.25],eye(2),[0;0],zeros(2,2),[0;0],'Ts',0)
m.as = [0 1; 0 NaN]
m.bs = [0 ;NaN]
m.cs = m.c
m.ds = m.d
m.ks = m.k
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m.x0s = [NaN;0]
m.noisevar = [0.01 0; 0 0.1]
The structure m can now be used to estimate the unknown parameters θ i from
observed data
Data = iddata([y1 y2], u, 0.1)
by
model = pem(Data,m)
The iterative search for minimum is then initialized at the parameters in the
nominal model m. The continuous time model is automatically sampled to agree
with the sampling interval of the data. The structure can also be used to
simulate the system above with sampling interval T=0.1 for input u and noise
realization e.
e = randn(300,2)
u = idinput(300);
simdat = iddata([],[u e],'Ts',0.1);
y = sim(m,simdat) % The continuous system will automatically be
% sampled using Ts =0.1
The nominal parameter values are then used, and the noise sequence is scaled
according to the matrix m.noisevar.
When estimating models, you can try a number of “neighboring” structures,
such as “what happens if I fix this parameter to a certain value” or “what
happens if I let loose these parameters.” This is easily handled by the structure
matrices As, Bs, etc. For example, to free the parameter x2(0) (perhaps the
motor wasn’t at rest after all), you can use
model = pem(Data,m,'x0s',[NaN;NaN])
To manipulate initial conditions, the function init is also useful.
State-Space Models with Coupled Parameters: the
idgrey Model
In some situations you may want the unknown parameters in the matrices in
(3-51) or (3-52) to be linked to each other. Then the NaN feature is not sufficient
to describe these links. Instead you need to do some “greybox” modeling and
write an M-file that describes the structure. The format is
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[A,B,C,D,K,x0] = mymfile(par,T,aux);
where mymfile is the user-defined name for the M-file, par contains the
parameters as a column vector, T is the sampling interval, and aux contains
auxiliary variables. The latter variables are used to house options, so that some
different cases can be tried out without having to edit the M-file. The matrices
A, B, C, D, K, and x0 refer either to the continuous time description (3-52) or to
the discrete-time description (3-51). When a continuous time description is
fitted to sampled data, the estimation routines perform the necessary sampling
of the model. To obtain the same structure as in the Example 3.2, you can do
the following.
function [A,B,C,D,K,x0] = mymfile(par,T,aux)
A = [0 1; 0 par(1)];
B = [0;par(2)];
C = eye(2);
D = zeros(2,2);
K = zeros(2,1);
x0 =[par(3);0];
Once the M-file has been written, the idgrey model m is defined by the command
m = idgrey('mymfile',par,'c',aux);
where par contains the nominal (initial) values of the corresponding entries in
the structure. 'c' signals that the underlying parametrization is continuous
time. aux contains the values of the auxiliary parameters. Note that T and aux
must be given as input arguments, even if they are not used by the code.
From here on, estimate models and evaluate results as for any other model
structure. Some further examples of user-defined model structures are given
below.
Some Examples of idgrey Model Structures
With user-defined structures, you have complete freedom in the choice of
models of linear systems. This section gives two examples of such structures.
Example 3.3: Heat Diffusion. Consider a system driven by the heat-diffusion
equation (see also Example 4.3 in Ljung (1999)).
This is a metal rod with a heat-diffusion coefficient κ , which is insulated at the
near end and heated by the power u (W) at the far end. The output of the
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system is the temperature at the near end. This system is described by a partial
differential equation in time and space. Replacing the space-second derivative
by a corresponding difference approximation gives a time-continuous
state-space model (3-52), where the dimension of x depends on the grid-size in
space used in the approximation. It is also desirable to be able to work with
different grid sizes without having to edit the model file. This is described by
the following M-file.
function [A,B,C,D,K,x0] = heatd(pars,T,aux)
Ngrid = aux(1); % Number of points in the space-discretization
L = aux(2); % Length of the rod
temp = aux(3); % Assuming uniform initial temperature of the rod
deltaL = L/Ngrid;
% Space interval
kappa = pars(1); % The heat-diffusion coefficient
htf = pars(2); % Heat transfer coefficient at far end of rod
A = zeros(Ngrid,Ngrid);
for kk = 2:Ngrid-1
A(kk,kk-1) = 1;
A(kk,kk) = -2;
A(kk,kk+1) = 1;
end
A(1,1) = -1; A(1,2) = 1; % Near end of rod insulated
A(Ngrid,Ngrid-1) = 1;
A(Ngrid,Ngrid) = -1;
A = A∗kappa/deltaL/deltaL;
B = zeros(Ngrid,1);
B(Ngrid,1) = htf/deltaL;
C = zeros(1,Ngrid);
C(1,1) = 1;
D = 0;
K = zeros(Ngrid,1);
x0 = temp∗ones(Ngrid,1);
You can then define the model by
m = idgrey('heatd',[0.27 1],'c',[10,1,22])
for a 10th order approximation of a heat rod one meter in length, with an initial
temperature of 22 degrees. The initial estimate of the heat conductivity is 0.27,
and of the heat transfer coefficient is 1.
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The model parameters are estimated by
me = pem(Data,m)
If you would like to try a finer grid, that is, take Ngrid larger, you can do this
easily with
me = pem(Data,m,'Filearg',[20,1,22])
Example 3.4: Parametrized Disturbance Models. Consider a discrete-time model
x ( t + 1 ) = Ax ( t ) + Bu ( t ) + w ( t )
y ( t ) = Cx ( t ) + e ( t )
where w and e are independent white noises with covariance matrices R1 and
R2, respectively. Suppose that you know the variance of the measurement
noise R2, and that only the first component of w ( t ) is nonzero. This can be
handled by the following M-file.
function [A,B,C,D,K,x0] = mynoise(par,T,aux)
R2 = aux(1); % The assumed known measurement noise variance
A = [par(1) par(2);1 0];
B = [1;0];
C = [par(3) par(4)];
D = 0;
R1 = [par(5) 0;0 0];
K = A∗dlqe(A,eye(2),C,R1,R2); % from the Control System Toolbox
x0 = [0;0];
State-Space Structures: Initial Values and Numerical
Derivatives
For a structured state-space model it is sometimes difficult to find good initial
parameter values at which to start the numerical search for a minimum of
(3-38). It is always best to use physical insight, whenever possible, to suggest
such values. For random initialization, the command init is useful. Since
there is always a risk that the numerical minimization may get stuck in a local
minimum, it is advisable to try several different initialization values for θ .
In the search for the minimum, the gradient of the prediction errors e ( t ) is
computed by numerical differentiation. The step-size is determined by the
M-file nuderst. In its default version the step-size is simply 10 – 4 times the
absolute value of the parameter in question (or the number 10 – 7 if this is
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larger). When the model structure contains parameters with different orders of
magnitude, try to scale the variables so that the parameters are all roughly the
same magnitude. You may need to edit the M-file nuderst to address the
problem of suitable step sizes for numerical differentiation.
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Examining Models
Once you have estimated a model, you need to investigate its properties. You
have to simulate it, test its predictions, and compute its poles and zeros and so
on. You thus have to transform the model to various ways of representing and
presenting it. This section deals with how this is done. The following topics will
be covered:
• Parametric models: basic use, accessing properties, simulation and
prediction. Also manipulating channels, in particular the noise input
channels.
• Frequency domain models
• Graphing model properties
• Transformations to other representations
• Transformations between continuous and discrete time
Parametric Models: idmodel and its children
idmodel is an object that the user does not deal with directly. It contains all the
common properties of the model objects idarx, idgrey, idpoly, and idss,
which are returned by the different estimation routines.
Basic Use
If you just estimate models from data, the model objects should be transparent.
All parametric estimation routines return idmodel results.
m = arx(Data,[2 2 1])
The model m contains all relevant information. Just typing m will give a brief
account of the model. present(m) also gives information about the
uncertainties of the estimated parameters. get(m) gives a complete list of
model properties.
Most of the interesting properties can be directly accessed by subreferencing.
m.a
m.da
See the property list obtained by get(m), as well as the property lists of
idgrey, idarx, idpoly, and idss in the “Command Reference” for more details
on this.
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The characteristics of the model m can be directly examined and displayed by
commands like impulse, step, bode, nyquist, pzmap. The quality of the model
is assessed by commands like compare, and resid. If you have the Control
System Toolbox, just typing view(m) gives access to various display functions.
More details about this will be given below.
To extract state-space matrices, transfer function polynomials, etc., there are
the commands
arxdata, polydata, tfdata, ssdata, zpkdata
and by idfrd and freqresp, the frequency response of the model can be
computed.
Simulation and Prediction
Any idmodel m can be simulated with
y = sim(m,Data)
where Data is an iddata object with just input channels.
Data = iddata([ ],[u v])
The number of input channels must either be equal to the number of measured
channels in m, in which case a noise free simulation is obtained, or equal to the
sum of the number of input and output channels in m. In the latter case the last
input signals (v) are interpreted as white noise. They are then scaled by the
NoiseVariance matrix of m and added to the output via the disturbance model
y = Gu + He
e = Lv
T
where the matrix L is given from the noise covariance Λ by Λ = LL .
L=chol(m.NoiseVariance)'
The output is returned as an iddata object with just output channels. Here is
a typical string of commands.
A = [1 -1.5 0.7];
B = [0 1 0.5];
m0 = idpoly(A,B,[1 -1 0.2]);
u = iddata([],idinput(400,'rbs',[0 0.3]));
v= iddata([],randn(400,1));
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y = sim(m0, [u v]);
plot(y)
The “inverse model” (3-38), which computes the prediction errors from given
input-output data, is simulated with
e = pe(m,[y u])
To compute the k-step ahead prediction of the output signal based on a model
m, the procedure is as follows.
yhat = predict(m,[y u],k)
The predicted value ŷ ( t t – k ) is computed using the information in u ( s ) up to
time s = t and information in y ( s ) up to time s = t – k . The actual way that
the information in past outputs is used depends on the disturbance model in m.
For example, an output error model (that is, H = 1 in (3-10) maintains that
there is no information in past outputs, therefore, predictions and simulations
coincide.
predict can evaluate how well a time-series model is capable of predicting
future values of the data. Here is an example, where y is the original series of,
say, monthly sales figures. A model is estimated based on the first half, and
then its ability to predict half a year ahead is checked out on the second half of
the observations.
plot(y)
y1 = y(1:48), y2 = y(49:96)
m4 = ar(y1,4)
yhat = predict(m4,y2,6)
plot(y2,yhat)
The command compare is useful for any comparisons involving sim and
predict.
Dealing with Input and Output Channels
For multivariable models, you construct submodels containing a subset of
inputs and outputs by simple subreferencing. The outputs and input channels
can be referenced according to
m(outputs,inputs)
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Use colon (:) to denote all channels and the empty matrix ([ ]) to denote no
channels. The channels can be referenced by number or by name. For several
names, a cell array must be used.
m3 = m('position',{'power','speed'})
or
m3 = m(3,[1 4])
Thus m3 is the model obtained from m by considering the transfer functions from
input numbers 1 and 4 (with input names 'power' and 'speed') to output
number 3 (with name ’position’)
For a single output model m
m4 = m(inputs)
will select the corresponding input channels, and for a single input model
m5 = m(outputs)
will select the indicated output channels.
Subreferencing is quite useful, e.g., when a plot of just some channels is
desired.
The Noise Channels
The estimated models have two kinds of input channels: the measured inputs
u and the noise inputs e. For a general linear model m, we have
y ( t ) = G ( q )u ( t ) + H ( q )e ( t )
(3-53)
where u is the nu-dimensional vector of measured input channels and e is the
ny-dimensional vector of noise channels. The covariance matrix of e is given by
the property 'NoiseVariance'. Occasionally this matrix Λ will be written in
factored form
Λ = LL
T
This means that e can be written as
e = Lv
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Examining Models
where v is white noise with identity covariance matrix (independent noise
sources with unit variances).
If m is a time series (nu = 0), G is empty and the model is given by
y ( t ) = H ( q )e ( t )
(3-54)
For the model m in (3-53), the restriction to the transfer function matrix G is
obtained by
m1 = m('measured') or just m1 = m('m')
Then e is set to 0 and H is removed.
Analogously
m2 = m('noise') or just m2 = m('n')
creates a time-series model m2 from m by ignoring the measured input. That is
m2 is given by (3-54).
For a system with measured inputs, bode, step, and many other
transformation and display functions just deal with the transfer function
matrix G. To obtain or graph the properties of the disturbance model H, it is
therefore important to make the transformations m('n'). For example,
bode(m('n'))
will plot the additive noise spectra according to the model m, while
bode(m)
just plots the frequency responses of G.
To study the noise contributions in more detail, it may be useful to convert the
noise channels to measured channels, using the command noisecnv:
m3 = noisecnv(m)
This creates a model m3 with all input channels, both measured u and noise
sources e, being treated as measured signals,. That is, m3 is a model from u and
e to y, describing the transfer functions G and H. The information about the
variance of the innovations e is then lost. For example, studying the step
response from the noise channels, will then not take into consideration how
large the noise contributions actually are.
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To include that information, e should first be normalized e = Lv , so that v
becomes white noise with an identity covariance matrix.
m4 = noisecnv(m,'Norm')
This will create a model m4 with u and v treated as measured signals.
y ( t ) = G ( q )u ( t ) + H ( q )Lv ( t ) = G HL u
v
For example, the step responses from v to y will now also reflect the typical size
of the disturbance influence, due to the scaling by L. In both these cases, the
previous noise sources, that have become regular inputs will automatically get
input names that are related to the corresponding output. The unnormalized
noise sources e have names like '[email protected]' (noise e at output channel with name
y1), while the normalized sources v are called '[email protected]'.
Retrieving Transfer Functions
The functions that retrieve transfer function properties, ssdata, tfdata, and
zpkdata will thus work as follows for a model (3-53) with measured inputs: (fcn
is any of ssdata, tfdata, or zpkdata).
fcn(m) returns the properties of G (ny outputs and nu inputs)
fcn(m('n')) returns the properties of the transfer function H (ny outputs and
ny inputs)
fcn(noisecnv(m)) returns the properties of the transfer function [G H] (ny
outputs and ny+nu inputs).
fcn(noisecnv(m,'Norm')) returns the properties of the transfer function
[G HL} (ny outputs and ny+nu inputs. Analogously
fcn(noisecnv(m('n'),'Norm'))
returns the properties of the transfer function HL. (ny outputs and ny inputs).
If m is a time series model, fcn(m) returns the properties of H , while
fcn(noisecnv(m,'Norm'))
returns the properties of HL.
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Note that the estimated covariance matrix NoiseVariance itself is uncertain.
This means that the uncertainty information about H is different from that of
HL.
idmodel Properties
See the “Command Reference” chapter for a complete list of idmodel
properties.
Adding Channels
m = [m1,m2,...,mN]
creates an idmodel object m, consisting of all the input channels in m1,... mN.
The output channels of mk must be the same. Analogously
m = [m1;m2;... ;mN]
creates an idmodel object m consisting of all the output channels in m1, m2,...,
mN. The input channels of mk must all be the same.
If you have the Control System Toolbox, interconnections between idmodels,
like G1+G2, G1*G2, append(G1,G2), feedback(G1,G2) , etc, can be performed
just as for LTI-objects. However, covariance information is typically lost.
Frequency Function Format: the idfrd model
Frequency functions and spectra are stored as an idfrd (Identified Frequency
Response Data) model object (which is not a child of idmodel). This model
format is used by spa and etfe to deliver their results. Moreover, any idmodel
can be transformed to an idfrd object.
The frequency function and the disturbance spectrum corresponding to an
idmodel m is computed by
h = idfrd(m)
ˆ
This gives G and Φ v in (3-11) along with their estimated covariances, which
are translated from the covariance matrix of the estimated parameters. If m
corresponds to a time-continuous model, the frequency functions are computed
accordingly.
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The functions are retrieved by h.ResponseData, h.CovarianceData,
h.SpectrumData, and h.NoiseCovariance or any case-insensitive
abbreviation of the names. The frequency vector is contained in h.Frequency.
In addition, an idfrd model can be defined directly from the frequency
functions. See the “Command Reference” chapter, which also contains a list of
idfrd properties. The channels of an idfrd model can be manipulated
analogously to idmodels.
An alternative is to compute the response functions without storing them as an
idfrd object.
[Response,Frequency,Covariance] = freqresp(m)
Graphs of Model Properties
There are several commands in the toolbox for graphing model characteristics
such as:
• bode
• compare
• ffplot
• impulse
• nyquist
• pzmap
• step
They have all the same basic syntax. To look at one model use
command(Model)
where command is any of the functions listed above.
command(Mod1,Mod2, ...,ModN)
shows a comparison of several models. Modk can be any idmodel models. They
can be used with any of the Control System Toolbox’s LTI models. For some
commands Modk can also be idfrd and iddata objects. For multivariable
models, the plots are grouped so that each input/output channel (for all models)
are plotted together. The InputName and OutputName properties of the models
are used for this. The number of channels need not be the same in the different
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models, which is quite useful when trying to find a good model of a
multivariable system.
command(Mod1,PlotStyle1,...,ModN,PlotStyleN)
allows you to define colors, linestyles and markers associated with the different
models. PlotStyle takes values such as 'b' (for blue), 'b:' (for a blue dotted
line) or 'b*-' (for a blue solid line with the points marked by a star). This is
the same as for the usual plot command.
To also show the uncertainty of the model characteristics, use
command(Mod1,...,ModN,'sd',SD)
This will mark, using dash-dotted lines, a confidence region around the
nominal model corresponding to SD standard deviations (for the Gaussian
distribution). This region is computed using the estimated covariance matrix
for the estimated parameters.
command(Mod1,...,ModN,'sd',SD,'fill')
shows the uncertainty region as a filled region instead.
The different commands have some further options to select time or frequency
ranges and similar. See the “Command Reference” chapter.
If Model contains measured input channels, the plot shows just the transfer
functions from these measured inputs to the outputs, that is G in (3-53). To
graph the response from the noise sources, use
command(Model('n'))
For the frequency response graphs, this shows the additive disturbance
spectra, i.e., the spectra of the signal H(q)e(t) in Equation (3-53), so that the
properties of the noise source e are included in the plot.
For the other graphs, the properties of the transfer function H are shown, i.e.,
no noise normalization is done. The same is true if Model is a time series and
has no measured input channels. That means that, for example, step shows
the step response of the transfer function H, without accounting for the size
(covariance matrix) of e. To include such effects, the disturbances should first
be converted to normalized noise sources, using the command noisecnv. See
“The Noise Channels” on page 3-52.
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Model Output
An important and visually suggestive plot, is to compare the measured output
signal with the models’ simulated or predicted outputs. This is achieved by
compare(Data,model)
The input signal in Data is used by the model(s) to simulate the output. This
simulated output is shown together with the measured output, which reveals
what features in the data the model can and cannot reproduce. Also a legend
shows the fit between the signals, in terms of how much of the output variation
is reproduced by the model(s).
Frequency Response
Three functions offer graphic display of the frequency functions and spectra:
bode, ffplot, and nyquist.
bode(G)
plots the Bode diagram of G (logarithmic scales and frequencies in rad/sec). If G
is a spectrum, only an amplitude plot (the power spectrum) is given. Here G can
be any idmodel or idfrd object.
The command ffplot has the same syntax as bode but works with linear
frequency scales and Hertz as the unit. The command nyquist also has the
same syntax, but produces Nyquist plots; i.e., graphs of the frequency function
in the complex plane.
Transient Response
The impulse and step responses of the models are shown by
impulse(Model) and step(Model)
impulse and step follow the general syntax, but can also accept iddata objects
as arguments. For direct estimation of step and impulse responses from data,
the procedure described in “Estimating Impulse Responses” on page 3-15 is
used.
Zeros and Poles
The zeros and poles are graphed by
pzmap(Model)
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This gives a plot with ‘x’ marking poles and ‘o’ marking zeros. Otherwise, pzmap
follows the general syntax.
General
If you have the Control System Toolbox
view(Model)
will open the LTI-viewer with access to a number of model displays. No
uncertainty information can be shown, though.
Transformations to Other Model Representations
Within the structure in which the model was created, you can extract
parametric information by get or by subscripting. For example, for a
state-space model, Mod.A is the A-matrix, while Mod.dA contains its standard
deviations. For a polynomial model, Mod.a and Mod.da are the A-polynomial
and its standard deviation, etc.
In addition, regardless of the particular model structure, there are a number of
commands that compute various model representations. These all have the
basic syntax
[G, dG] = command(Model)
where G contains model characteristics and dG their standard deviation or
covariance. The transformation commands are
[A,B,C,D,K,X0,dA,dB,dC,dD,dK,dX0] = ssdata(Model)
[a,b,c,d,f,da,db,dc,dd,df] = polydata(Model)
[A,B,dA,dB] =arxdata(Model)
[Num,Den,dNum,dDen] = tfdata(Model)
[Z,P,K,CovZ,CovP,covK] = zpkdata(Model)
G = idfrd(Model)
[H,w,CovH] = freqresp(Model)
The two last commands were described on page 3-55. The three first commands
clearly transform to the state-space, the polynomial, and the multivariable
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ARX representations. See “Defining Model Structures” on page 3-35. tfdata
and zpkdata compute the transfer functions and zeros, poles and transfer
function gains. See the reference pages in Chapter 4 for details.
Discrete and Continuous Time Models
Continuous-Time Models
Continuous-time models are created and recognized by the property 'Ts' = 0.
All idmodel objects can be created and analyzed as continuous-time models by
setting Ts equal to zero at the time of creation, as in
m = idpoly(1,[0 1 1],1,1,[1 2 3],'Ts',0)
for the model
s+1
y = ---------------------------- u + e
2
s + 2s + 3
All model characteristics are then computed and graphed for the
continuous-time representation. Time and frequency scales are determined
using the sampling interval of the data, from which the model was estimated.
For a nonestimated model, a default choice is made, which may make it
necessary to supply frequency and time ranges to the commands.
For simulation and prediction, the continuous-time models are first converted
to discrete time, using the sampling interval and intersample behavior of the
data.
Estimating Continuous-Time Models
The estimation routines support the estimation of continuous-time state-space
models in several different ways. The easiest is to use
mc = n4sid(Data,nx,'Ts',0)
This creates a continuous-time model in a free parameterization, based on the
n4sid estimate. Further iterations from this estimate can be achieved by
mc = pem(Data,mc,'ss','can')
or directly by
mc = pem(Data,nx,'Ts',0,'ss','can')
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The search for the continuous-time model must be carried out in a canonical (or
any other structured) parameterization. The fit is still made to the sampled
signals in Data. The model is sampled with the data’s sampling interval for the
fit. The information about the input intersample behavior in Data.
InterSample is used to determine whether the sampling should be
zero-order-hold (zoh, piecewise constant input) or first-order-hold (foh,
piecewise linear input). All this gives black-box state-space models without any
prescribed internal structure. In these cases, and for a zoh input, it may be
easier to first estimate a black-box model in discrete time and then transform
it to continuous time with d2c as described below. For a foh input it might be
better to directly estimate the continuous-time model, since the mapping from
discrete to continuous under a foh assumption is somewhat tricky.
The major reason for identifying continuous-time model is to secure a
particular structure of the continuous-time state-space matrices. This would
typically reflect a physical interpretation or some greybox modeling work done.
This situation is handled by defining the structure as a continuous time idss
or idgrey model, as described in“Black-Box State-Space Models: the idss
Model” on page 3-39 and onwards. The resulting structure mi is fitted to data
in the usual way.
m = pem(Data,mi)
Transformations
Transformations between continuous-time and discrete-time model
representations are achieved by c2d and d2c. Note that it is not sufficient to
just assign a new value of Ts to the model object. The corresponding
uncertainty measure (the estimated covariance matrix of the internal
parameters) is also transformed in most cases. The syntax is
modc = d2c(modd)
modd = c2d(mc,T)
If the discrete-time model has some pure time delays (nk > 1 ) the default
command removes them before forming the continuous-time model, and
appends them using the property InputDelay in model modc. This property is
used to add appropriate phase lag and shift the data, whenever the model is
used. d2c also offers as an option to approximate the dead time by a finite
dimensional system. Note that the disturbance properties are translated by the
somewhat questionable formula (3-29). The covariance matrix is translated by
Gauss’ approximation formula using numerical derivatives. The M-file
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nuderst is then invoked. You may want to edit it for applications where the
parameters have very different orders of magnitude. See the comments in
“State-Space Structures: Initial Values and Numerical Derivatives” on
page 3-47.
Here is an example that compares the Bode plots of an estimated model and its
continuous-time counterpart.
m= armax(Data,[2 3 1 2]);
mc = d2c(m); bode(m,mc)
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Model Structure Selection and Validation
After you have been analyzing data for some time, you typically end up with a
large collection of models with different orders and structures. You need to
decide which one is best, and if the best description is an adequate model for
your purposes. These are the problems of model validation.
Model validation is the heart of the identification problem, but there is no
absolute procedure for approaching it. It is wise to be equipped with a variety
of different tools with which to evaluate model qualities. This section describes
the techniques you can use to evaluate model qualities using the System
Identification Toolbox.
Comparing Different Structures
It is natural to compare the results obtained from model structures with
different orders. For state-space models this is easily obtained by using a vector
argument for the order in n4sid or pem.
m = n4sid(Data,1:10)
m = pem(Data,'nx',3:15)
This invokes a plot from which a “best” order is chosen. Just omitting the order
argument, m = n4sid(Data) or pem(Data) makes a default choice of the best
order.
For models of ARX type, various orders and delays can be efficiently studied
with the command arxstruc. Collect in a matrix NN all of the ARX structures
you want to investigate, so that each row of NN is of the type
[na nb nk]
With
V = arxstruc(Date,Datv,NN)
an ARX model is fitted to the data set Date for each of the structures in NN.
Next, for each of these models, the sum of squared prediction errors is
computed, as they are applied to the data set Datv. The resulting loss functions
are stored in V together with the corresponding structures.
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To select the structure that has the smallest loss function for the validation set
Datv, use
nn = selstruc(V,0)
Such a procedure is known as cross validation and is a good way to approach
the model selection problem.
It is usually a good idea to visually inspect how the fit changes with the number
of estimated parameters. A graph of the fit versus the number of parameters is
obtained with
selstruc(V)
This routine prompts you to choose the number of parameters to estimate,
based upon visual inspection of the graph, and then it selects the structure
with the best fit for that number of parameters.
The command struc helps generate typical structure matrices NN for
single-input systems. A typical sequence of commands is
V = arxstruc(Date,Datv,struc(2,2,1:10));
nn = selstruc(V,0);
nk = nn(3);
V = arxstruc(Date,Datv,struc(1:5,1:5,nk-1:nk+1));
selstruc(V)
where you first establish a suitable value of the delay nk by testing second
order models with delays between one and ten. The best fit selects the delay,
and then all combinations of ARX models with up to five a and b parameters
are tested with delays around the chosen value (a total of 75 models).
If the model is validated on the same data set from which it was estimated; i.e.,
if Date = Datv, the fit always improves as the flexibility of the model structure
increases. You need to compensate for this automatic decrease of the loss
functions. There are several approaches for this. Probably the best known
technique is Akaike’s Final Prediction Error (FPE) criterion and his closely
related Information Theoretic Criterion (AIC). Both simulate the cross
validation situation, where the model is tested on another data set.
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The FPE is formed as
d
1 + ---N
FPE = -------------- V
d
1 – ---N
where d is the total number of estimated parameters and N is the length of the
data record. V is the loss function (quadratic fit) for the structure in question.
The AIC is formed as
d
AIC = log  V  1 + 2 ---- 
N
(See Section 16.4 in Ljung (1999).)
According to Akaike’s theory, in a collection of different models, choose the one
with the smallest FPE (or AIC). The FPE values are displayed with the model
parameters, by just typing the model name. It is also one of the fields in
EstimationInfo, and can be accessed by
FPE = fpe(m)
Similarly, the AIC value of an estimated model is obtained as
AIC = aic(m)
The structure that minimizes the AIC is obtained with
nn = selstruc(V,'AIC')
where V was generated by arxstruc.
A related criterion is Rissanen’s Minimum Description Length (MDL)
approach, which selects the structure that allows the shortest over-all
description of the observed data. This is obtained with
nn = selstruc(V,'MDL')
If substantial noise is present, the ARX models may need to be of high order to
describe simultaneously the noise characteristics and the system dynamics.
(For ARX models the disturbance model 1/A(q) is directly coupled to the
dynamics model B(q)/A(q).)
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Impulse Response to Determine Delays
The command impulse applied to a dataset
impulse(Data,'sd',3)
shows a nonparametric estimate of the impulse response. In the call above, also
a confidence region around zero is shown, corresponding to three standard
deviations (ca 99.9%). Any part of the impulse response that is outside this
region is thus significant. The first sample after t=0, at which the impulse
response estimate crosses the confidence band is thus a good estimate of the
delay in the channel in question.
Significant impulse response estimates for negative time lags are indications
of feedback in the data.
Checking Pole-Zero Cancellations
A near pole-zero cancellation in the dynamics model is an indication that the
model order may be too high. To judge if a “near” cancellation is a real
cancellation, take the uncertainties in the pole- and zero-locations into
consideration
pzmap(mod,'sd',1)
where the 1 indicates how many standard-deviations wide the confidence
interval is. If the confidence regions of a zero and a pole overlap, try lower
model orders.
This check is especially useful when the models have been generated by arx.
As mentioned on page 3-66, the orders can be pushed up because of the
requirement that 1/A(q) describe the disturbance characteristics. Checking
cancellations in B(q)/A(q) then gives a good indication of which orders to chose
from model structures like armax, oe, and bj.
Residual Analysis
The residuals associated with the data and a given model, as in (3-38), are
ideally white and independent of the input for the model to correctly describe
the system. The function
resid(Model,Data)
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computes the residuals (prediction errors) e from the model when applied to
Data, and performs whiteness and independence analyses. The auto
correlation function of e and the cross-correlation function between e and u are
computed and displayed for up to lag 25. Also displayed are 99% confidence
intervals for these variables, assuming that e is indeed white and independent
of u.
The rule is that if the correlation functions go significantly outside these
confidence intervals, do not accept the corresponding model as a good
description of the system. Some qualifications of this statement are necessary:
• Model structures like the OE structure (3-17) and methods like the IV
method (3-41) focus on the dynamics G and less about the disturbance
properties H. If you are interested primarily in G, focus on the independence
of e and u rather than the whiteness of e.
• Correlation between e and u for negative lags, or current e ( t ) affecting
future u ( t ) , is an indication of output feedback. This is not a reason to reject
the model. Correlation at negative lags is of interest, since certain methods
do not work well when feedback is present in the input-output data, (see
“Feedback in Data” on page 3-76), but concentrate on the positive lags in the
cross-correlation plot for model validation purposes.
• When using the ARX model (3-14), the least squares procedure automatically
makes the correlation between e ( t ) and u ( t – k ) zero for k = nk , nk + 1 ,
…nk + nb – 1 , for the data used for the estimation.
The residuals e together with the input u are returned by
E= resid(Model,Data)
as an iddata object. As part of the validation process, you can graph the
residuals using
plot(E)
for a simple visual inspection of irregularities and outliers. (See also “Outliers
and Bad Data; Multi-Experiment Data” on page 3-74.)
Model Error Models
The residual call
E= resid(Model,Data)
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returns the iddata object e which has the inputs in Data as inputs and the
prediction errors (residuals) as outputs. Building models using e will thus
reveal if there is any significant influence from u to e left in the data. Such
models are called Model Error Models, and examining them is a good
complement to traditional residual analysis.
E= resid(Model,Data)
impulse(E,'sd',3) % An alternative to residual analysis
bode(spa(E),'sd',3) % Shows the frequency ranges
% with significant model errors
m = arx(E,[0 10 0])
bode(m,'sd',3)
Note that the resid command has several options to display model error
properties rather than correlation functions.
Noise-Free Simulations
To check whether a model is capable of reproducing the observed output when
driven by the actual input, you can run a simulation.
u = Data(:,[],:) % Extracting the input from the data
yh = sim(Model,u)
y = Data(:,:,[]) % extracting the output from the data
plot(y,yh)
The same result is obtained by
compare(Data,Model)
It is a much tougher and revealing test to perform this simulation, as well as
the residual tests, on a fresh data set Data that was not used for the estimation
of the model Model. This is called cross validation.
Assessing the Model Uncertainty
The estimated model is always uncertain, due to disturbances in the observed
data, and due to the lack of an absolutely correct model structure. The
variability of the model that is due to the random disturbances in the output is
estimated by most of the estimation procedures, and it can be displayed and
illuminated in a number of ways. This variability answers the question of how
different can the model be if the identification procedure is repeated, using the
same model structure, but with a different data set that uses the same input
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sequence. It does not account for systematic errors due to an inadequate choice
of model structure. There is no guarantee that the “true system” lies in the
confidence interval.
The uncertainty in the different model views is displayed if the argument 's d'
is included in the argument list
command(Model,'sd',sd)
as explained in “Graphs of Model Properties” on page 3-56.
The uncertainty in the time response is displayed by
simsd(Model,u)
Ten possible models are drawn from the asymptotic distribution of the model
Model. The response of each of them to the input u is graphed on the same
diagram.
The uncertainty of these responses concerns the external, input-output
properties of the model. It reflects the effects of inadequate excitation and the
presence of disturbances.
You can also directly get the standard deviation of the simulated result by
[ysim,ysimsd] = sim(Model,u)
The uncertainty in internal representations is manifested in the covariance
matrix of the estimated parameters
Model.CovarianceMatrix
which is used to give the standard deviations of all model characteristics. The
parametric uncertainty is directly available as
Model.da for the standard deviations of Model.a
Note that state-space models, estimated in a free parameterization do not have
well defined standard deviations of the matrix elements. The model still has
stored the uncertainty of the input-output behavior, so other model
representations and graphs will show the uncertainty. For a state-space model
in a free parameterization, it is possible to first transform it to a canonical
parameterization and then display the matrix parameter uncertainties.
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Modelc = Model
Modelc.ss = 'canon'
Modelc.da
All routines for computing and displaying model characteristics also offer to
calculate and show the uncertainties. See “Transformations to Other Model
Representations” on page 3-59.
Large uncertainties in these representations are caused by excessively high
model orders, inadequate excitation, or bad signal-to-noise ratios.
Comparing Different Models
It is a good idea to display the model properties in terms of quantities that have
more physical meaning than the parameters themselves. Bode plots, pole-zero
plots, and model simulations all give a sense of the properties of the system
that have been picked up by the model.
If several models of different characters give very similar Bode plots in the
frequency range of interest, you can be fairly confident that these must reflect
features of the true, unknown system. You can then choose the simplest model
among these.
A typical identification session includes estimation in several different
structures, and comparisons of the model properties. Here is an example.
a1 = arx(Data,[1 2 1]);
g = spa(Data);
bode(g,a1)
bode(g('n'),a1('n'))% the output disturbance spectra
am2 = armax(Data,[2 2 2 1]);
bode(g,am2)
pzmap(a1,am2,'sd',3)
Selecting Model Structures for Multivariable
Systems
A multivariable (MIMO) system is a system with several input and output
channels. All model structures in the toolbox support models with one output
and several inputs. Polynomial models, idpoly, do not handle multi-output
models, however.
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Model Structures
Multivariable system offer a potentially richer internal structure. The easiest
approach, in the black-box situation, is to think just in terms of input delays
and state-space model order. A recommended approach is to get an idea of
input delays from the nonparametric impulse response estimate, and
determine the vector nk = [nk1,nk2,...,nkm] where nkj is the minimal delay
from input j to any of the output channels, and then try state-space models
with several orders and with these delays.
impulse(Data,'sd',3)
Model = n4sid(Data(1:500),'nx',1:10,'nk',nk)
compare(Data(501:1000),Model)
The compare plot will reveal which output channels are easy and which are
difficult to reproduce.
An alternative to find the delays is to first estimate a parametric model with
delays 1, and then examine the impulse responses of this model and determine
the delays.
Model = pem(Data) % This uses 'best' model order
impulse(Model,'sd',3)
Model = pem(Data,'nx',1:10,'nk',nk)
(To test models with delay 0 in a similar way, use Model =
pem(Data,'best','nk',zeros(size(nk))). Significant responses at delay 0
must be examined with care, since they may be caused by feedback.)
Note that delays nk larger than 1 will be incorporated in the model structure,
and thus increase the state-space model order from the nominal one with
sum(max(nk-1,zeros(size(nk)))). An alternative is to use the property
'InputDelay'. This leads to a model that has the same delays as for ’nk', but
these are not explicitly shown in the model matrices, but stored as a property
to be used when necessary. See idmodel properties in the “Command
Reference” chapter.
If you have detailed knowledge about which orders and delays that are
reasonable in the different input/output channels, you can use multivariable
ARX models, in the idarx model format. This allows you to define the orders of
the input and output lags, as well as the delays, independently for the different
channels.
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Black-box parameterizations of multi-variable systems require many
parameters. Therefore, it may be important to incorporate any essential
structure knowledge based on physical insight. This is typically done by
continuous-time, taylor-made model parameterizations using structured idss
models, or idgrey models. See “Structured State-Space Models with Free
Parameters: the idss Model” on page 3-42 and “State-Space Models with
Coupled Parameters: the idgrey Model” on page 3-44.
Channel Selection
A particular aspect of multivariable models regards the selection of channels.
Models for subselections of input-output channels may be quite useful and
informative. Generally speaking the models become “better” when more input
channels are used, and “worse” when more output channels are used. The
latter observation is due to the fact that such models have “more to explain.”
If you build models with several outputs and find, using compare, a certain
output channel to be difficult to reproduce, then try to build model of this
channel alone. This will reveal if there are inherent difficulties with this
output, or that it is just too difficult to handle it together with other outputs.
Analogously, if you see that, using, e.g., step or impulse, a certain input
channel seems to have an insignificant influence on the outputs, then remove
that channel, and examine if the corresponding model becomes any worse, e.g.,
in the compare plots.
A main feature of the toolbox’s data and model objects is that it gives full
support for the bookkeeping required for these channel subselections.
Channels are selected by direct subreferencing, and the InputName and
OutputName properties form the basis for a correct combination of channels.
The subreferencing follows
Data(Samples,Outputs,Inputs)
Model(Outputs,Inputs)
and typical command sequences may be
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Date = Data(1:500)
Datv = Data(501:1000)
m = pem(Date)
compare(Datv,m)
m1 = pem(Date(:,3,4))
compare(Datv,m,m1)
bode(m,m1)
compare(Datv,m(:,4),m1)
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Dealing with Data
Extracting information from data is not an entirely straightforward task. In
addition to the decisions required for model structure selection and validation,
the data may need to be handled carefully. This section gives some advice on
handling several common situations.
Offset Levels
When the data have been collected from a physical plant, they are typically
measured in physical units. The levels in these raw input and output
measurements may not match in any consistent way. This will force the models
to waste some parameters correcting the levels.
Typically, linearized models are sought around some physical equilibrium. In
such cases offsets are easily dealt with: subtract the mean levels from the input
and output sequences before the estimation. It is best if the mean levels
correspond to the physical equilibrium, but if such values are not known, use
the sample means.
Data = detrend(Data);
Section 14.1 in Ljung (1999) discusses this in more detail. There are situations
when it is not advisable to remove the sample means. It could for example be
that the physical levels are built into the underlying model, or that
integrations in the system must be handled with the right level of the input
being integrated.
With the detrend command, you can also remove piece-wise linear trends.
Outliers and Bad Data; Multi-Experiment Data
Real data are also subject to possible bad disturbances; an unusually large
disturbance, a temporary sensor or transmitter failure, etc. It is important that
such outliers are not allowed to affect the models too strongly.
The robustification of the error criterion (described under LimitError on page
3-33) helps here, but it is always good practice to examine the residuals for
unusually large values, and to go back and critically evaluate the original data
responsible for the large values. If the raw data are obviously in error, they can
be smoothed, and the estimation procedure repeated.
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Often the data has portions with bad behavior. This may, e.g., be due to big
disturbances or sensor failures over a period of time. It can also be that there
are time periods where “nothing happens,” the input is not exciting, etc. Then
the best alternative is to break up the data into pieces of informative portions.
By merging the pieces into a multiexperiment iddata object, they can still be
used together to build models. Another situation when multiexperiment data
is useful is when several different experiments have been performed on the
same plant. The estimation routines take proper action to handle the different
pieces. All estimation, simulation, and validation routines in the toolbox
handle multi-experiment data in a transparent fashion. A typical string of
commands could be
plot(Data)
Datam = merge(Data(1:340),Data(500:897), ...
Data(1001:1200),Data(1550:2000))
m =pem(Datam{[1,2,4]}) % Portions 1,2 and 4 for estimation
compare(Datam{3},m) % Portion 3 for validation
Missing Data
In practice it is often the case that certain measurement samples are missing.
The reason may be sensor failures or data acquisition failures. It may be that
the data are directly reported as missing, or that plots reveal that some values
are obviously in error. This may apply both to inputs and outputs. In these
cases, replace the missing data by NaN when forming the signal matrices and
the iddata object. The routine misdata can then be applied to reconstruct the
missing data in a reasonable way.
dat = iddata(y,u,0.2) % y and/or u contain NaN for missing data.
dat1 = misdata(dat);
plot(dat,dat1) % Checking how the missing data
% have been estimated in dat1
m = pem(dat1) % Model estimated using reconstructed missing data
See Section 14.2 in Ljung(1999) for a discussion on missing data.
Filtering Data: Focus
Depending upon the application, interest in the model can be focused on
specific frequency bands. Filtering the data before the estimation, through
filters that enhance these bands, improves the fit in the interesting regions.
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This is accomplished in the System Identification Toolbox by the property
'Focus'. For example, to enhance the fit in the frequency band between 0.02π
and 0.1π, (assuming a unit sampling interval) execute
[B,A] = butter(5,[0.02 0.1])
m = pem(Data,3,'Foc',{B,A})
ma = arx(Data,[2 3 1],'Foc',{B,A})
This computes and uses a fifth order Butterworth bandpass filter with
passband between the indicated frequencies. The data is filtered through this
filter before fitting the transfer function from the measured inputs (G in
Equation (3-53)) to the outputs. The disturbance model (H) is however
estimated using the unfiltered data. Chapter 14 in Ljung (1999) discusses the
role of filtering in more detail.
The command butter is from the Signal Processing Toolbox. If you do not have
that toolbox, the filter can be computed using idfilt from the System
Identification Toolbox.
[Df,Mfilt] = idfilt(Data,5,[0.02 0.1])
m = pem(Data,3,'Foc',Mfilt)
For a model that does not use a disturbance description (that is, H = 1 in (3-53),
which corresponds to K = 0 for state-space, and na=nc=nd=0 for polynomial
models), the Focus effect is the same as applying the routine to filtered data.
That is,
m = pem(Data,3,'Foc',Mfilt,'dist','none')
m = pem(Df,3,'dist','none')
give the same model.
The System Identification Toolbox contains other useful commands for related
problems. For example, if you want to lower the sampling rate by a factor of 5,
use
Dat5 = resample(Data,1,5);
Feedback in Data
If the system was operating in closed loop (feedback from the past outputs to
the current input) when the data were collected, some care has to be exercised.
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Basically, all the prediction error methods work equally well for closed-loop
data. Note, however, that the Output-Error model (3-17) and the Box-Jenkins
model (3-18) are normally capable of giving a correct description of the
dynamics G, even if H (which equals 1 for the output error model) does not
allow a correct description of the disturbance properties. This is no longer true
for closed-loop data. You then need to model the disturbance properties more
carefully. Another thing to be cautious about is that impulse response effects
at delay 0 very well could be traced to the feedback mechanism and not to the
system itself.
The spectral analysis method and the instrumental variable techniques (with
default instruments) as well as n4sid may give unreliable results when
applied to closed-loop data. These techniques should be avoided when feedback
is present.
To detect if feedback is present, use the basic method of applying impulse to
estimate the impulse response. Significant values of the impulse response at
negative lags is a clear indication of feedback. When a parametric model has
been estimated and the resid command is applied, a graph of the correlation
between residuals and inputs is given. Significant correlation at negative lags
again indicates output feedback in the generation of the input. Testing for
feedback is, therefore, a natural part of model validation.
Delays
The selection of the delay nk in the model structure is a very important step in
obtaining good identification results. You can get an idea about the delays in
the system by the impulse response estimate from impulse.
Incorrect delays are also visible in parametric models. Underestimated delays
(nk too small) show up as small values of leading b k estimates, compared to
their standard deviations. Overestimated delays (nk too large) are usually
visible as a significant correlation between the residuals and the input at the
lags corresponding to the missing b k terms in the resid plot.
A good procedure is to start by using arxstruc to test all feasible delays
together with a second-order model. Use the delay that gives the best fit for
further modeling. When you have found an otherwise satisfactory structure,
vary nk around the nominal value within the structure, and evaluate the
results.
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Recursive Parameter Estimation
In many cases it may be necessary to estimate a model on line at the same time
as the input-output data is received. You may need the model to make some
decision on line, as in adaptive control, adaptive filtering, or adaptive
prediction. It may be necessary to investigate possible time variation in the
system’s (or signal’s) properties during the collection of data. Terms like
recursive identification, adaptive parameter estimation, sequential estimation,
and online algorithms are used for such algorithms. Chapter 11 in Ljung
(1999) deals with such algorithms in some detail.
The Basic Algorithm
A typical recursive identification algorithm is
θ̂ ( t ) = θ̂ ( t – 1 ) + K ( t ) ( y ( t ) – ŷ ( t ) )
(3-55)
Here θ̂ ( t ) is the parameter estimate at time t, and y ( t ) is the observed output
at time t. Moreover, ŷ ( t ) is a prediction of the value y ( t ) based on observations
up to time t – 1 and also based on the current model (and possibly also earlier
ones) at time t – 1 . The gain K ( t ) determines in what way the current
prediction error y ( t ) – ŷ ( t ) affects the update of the parameter estimate. It is
typically chosen as
K ( t ) = Q ( t )ψ ( t )
(3-56)
where ψ ( t ) is (an approximation of) the gradient with respect to θ of ŷ ( t θ ) .
The latter symbol is the prediction of y ( t ) according the model described by θ .
Note that model structures like AR and ARX that correspond to linear
regressions can be written as
T
y ( t ) = ψ ( t )θ 0 ( t ) + e ( t )
(3-57)
where the regression vector ψ ( t ) contains old values of observed inputs and
outputs, and θ 0 ( t ) represents the true description of the system. Moreover,
e ( t ) is the noise source (the innovations). Compare with (3-14). The natural
T
prediction is ŷ ( t ) = ψ ( t )θ̂ ( t – 1 ) and its gradient with respect to θ becomes
exactly ψ ( t ) .
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For models that cannot be written as linear regressions, you cannot recursively
compute the exact prediction and its gradient for the current estimate θ̂ ( t – 1 ) .
Then approximations ŷ ( t ) and ψ ( t ) must be used instead. Section 11.4 in
Ljung (1999) describes suitable ways of computing such approximations for
general model structures.
The matrix Q ( t ) that affects both the adaptation gain and the direction in
which the updates are made, can be chosen in several different ways. This is
discussed in the following.
Choosing an Adaptation Mechanism and Gain
The most logical approach to the adaptation problem is to assume a certain
model for how the “true” parameters θ 0 change. A typical choice is to describe
these parameters as a random walk.
θ0( t ) = θ0 ( t – 1 ) + w( t )
(3-58)
Here w ( t ) is assumed to be white Gaussian noise with covariance matrix
T
Ew ( t )w ( t ) = R 1
(3-59)
Suppose that the underlying description of the observations is a linear
regression (3-57). An optimal choice of Q ( t ) in (3-55)-(3-56) can then be
computed from the Kalman filter, and the complete algorithm becomes
θ̂ ( t ) = θ̂ ( t – 1 ) + K ( t ) ( y ( t ) – ŷ ( t ) )
T
ŷ ( t ) = ψ ( t )θ̂ ( t – 1 )
K ( t ) = Q ( t )ψ ( t )
P( t – 1 )
Q ( t ) = ------------------------------------------------------------T
R 2 + ψ ( t ) P ( t – 1 )ψ ( t )
(3-60)
T
P ( t – 1 )ψ ( t )ψ ( t ) P ( t – 1 )
P ( t ) = P ( t – 1 ) + R 1 – -------------------------------------------------------------------T
R 2 + ψ ( t ) P ( t – 1 )ψ ( t )
2 10-7
Here R 2 is the variance of the innovations e ( t ) in (3-57): R 2 = Ee ( t ) (a
scalar). The algorithm (3-60) will be called the Kalman filter (KF) approach
to adaptation, with drift matrix R 1 . See eq (11.66)-(11.67) in Ljung (1999). The
algorithm is entirely specified by R 1 , R 2 , P ( 0 ) , θ ( 0 ) , and the sequence of data
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y ( t ) , ψ ( t ) , t = 1 , 2,…. Even though the algorithm was motivated for a linear
regression model structure, it can also be applied in the general case where
ŷ ( t ) is computed in a different way from (3-60b).
Another approach is to discount old measurements exponentially, so that an
τ
observation that is τ samples old carries a weight that is λ of the weight of
the most recent observation. This means that the following function is
minimized rather than (3-39)
t
∑λ
t–k 2
e (k)
(3-61)
k=1
at time t. Here λ is a positive number (slightly) less than 1. The measurements
that are older than τ = 1 ⁄ ( 1 – λ ) carry a weight in the expression above that
is less than about 0.3. Think of τ = 1 ⁄ ( 1 – λ ) as the memory horizon of the
approach. Typical values of λ are in the range 0.97– 0.995.
The criterion (3-61) can be minimized exactly in the linear regression case
giving the algorithm (3-60abc) with the following choice of Q ( t ) .
P( t – 1)
Q ( t ) = P ( t ) = --------------------------------------------------------T
λ + ψ ( t ) P ( t – 1 )ψ ( t )
T
1
P ( t – 1 )ψ ( t )ψ ( t ) P ( t – 1 )
P ( t ) = ---  P ( t – 1 ) – --------------------------------------------------------------------
T
λ
λ + ψ ( t ) P ( t – 1 )ψ ( t ) 
(3-62)
This algorithm will be called the Forgetting Factor (FF) approach to
adaptation, with the forgetting factor λ . See eq (11.63) in Ljung (1999). The
algorithm is also known as recursive least squares (RLS) in the linear
regression case. Note that λ = 1 in this approach gives the same algorithm as
R 1 = 0, R 2 = 1 in the Kalman filter approach.
A third approach is to allow the matrix Q ( t ) to be a multiple of the identity
matrix.
Q ( t ) = γI
(3-63)
It can also be normalized with respect to the size of ψ .
γ
Q ( t ) = ----------------- I
2
ψ(t)
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(3-64)
Recursive Parameter Estimation
See eqs (11.45) and (11.46), respectively in Ljung (1999). These choices of Q ( t )
move the updates of θ̂ in (3-55) in the negative gradient direction (with respect
to θ ) of the criterion (3-39). Therefore, (3-63) will be called the Unnormalized
Gradient (UG) approach and (3-64) the Normalized Gradient (NG)
approach to adaptation, with gain γ . The gradient methods are also known as
least mean squares (LMS) in the linear regression case.
Available Algorithms
The System Identification Toolbox provides the following functions that
implement all common recursive identification algorithms for model structures
in the family (3-43): rarmax, rarx, rbj, rpem, rplr, and roe. They all share the
following basic syntax.
[thm,yh] = rfcn(z,nn,adm,adg)
Here z contains the output-input data as usual. nn specifies the model
structure, exactly as for the corresponding offline algorithm. The arguments
adm and adg select the adaptation mechanism and adaptation gain listed above.
adm = 'ff'; adg = lam
gives the forgetting factor algorithm (3-62), with forgetting factor lam.
adm = 'ug'; adg = gam
gives the unnormalized gradient approach (3-63) with gain gam. Similarly,
adm = 'ng'; adg = gam
gives the normalized gradient approach (3-64). To obtain the Kalman filter
approach (3-60) with drift matrix R1, enter
adm = 'kf'; adg = R1
The value of R 2 is always 1. Note that the estimates θ̂ in (3-60) are not affected
if all the matrices R 1, R 2 and P ( 0 ) are scaled by the same number. You can
therefore always scale the original problem so that R 2 becomes 1.
The output argument thm is a matrix that contains the current models at the
different samples. Row k of thm contains the model parameters, in
alphabetical order at sample time k, corresponding to row k in the data matrix
z. The ordering of the parameters is the same as m.par would give when
applied to a corresponding offline model.
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The output argument yh is a column vector that contains, in row k, the
predicted value of y ( k ) , based on past observations and current model. The
vector yh thus contains the adaptive predictions of the outputs, and is useful
also for noise cancelling and other adaptive filtering applications.
The functions also have optional input arguments that allow the specification
of θ ( 0 ), P ( 0 ) , and ψ ( 0 ) . Optional output arguments include the last value of
the matrix P and of the vector ψ .
Now, rarx is a recursive variant of arx; similarly rarmax is the recursive
counterpart of armax and so on. Note, though that rarx does not handle
multi-output systems, and rpem does not handle state-space structures.
The function rplr is a variant of rpem, and uses a different approximation of
the gradient ψ . It is known as the recursive pseudo-linear regression approach,
and contains some well known special cases. See Equation (11.57) in Ljung
(1999). When applied to the output error model (nn=[0 nb 0 0 nf nk]) it
results in methods known as HARF (’ff’-case) and SHARF (’ng’-case). The
common extended least squares (ELS) method is an rplr algorithm for the
ARMAX model (nn=[na nb nc 0 0 nk]).
The following example shows a second order output error model, which is built
recursively and its time varying parameter estimates plotted as functions of
time.
thm = roe(z,[2 2 1],'ff',0.98);
plot(thm)
The next example shows how a second order ARMAX model is recursively
estimated by the ELS method, using Kalman filter adaptation. The resulting
static gains of the estimated models are then plotted as a function of time.
[N,dum]=size(z);
thm = rplr(z,[2 2 2 0 0 1],'kf',0.01∗eye(6));
nums = sum(thm(:,3:4)')';
dens = ones(N,1)+sum(thm(:,1:2)')';
stg = nums./dens;
plot(stg)
So far, the examples of applications where a batch of data is examined cover
studies of the variability of the system. The algorithms are, however, also
prepared for true online applications, where the computed model is used for
some online decision. This is accomplished by storing the update information
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Recursive Parameter Estimation
in θ̂ ( t – 1 ), P ( t – 1 ) and information about past data in φ ( t – 1 ) (and ψ ( t – 1 ) )
and using that information as initial data for the next time step. The following
example shows the recursive least squares algorithm being used on line (just
to plot one current parameter estimate).
%Initialization, first i/o pair y,u (scalars)
[th,yh,P,phi] = rarx([y u],[2 2 1],'ff',0.98);
axis([1 50 -2 2])
plot(1,th(1),'∗'),hold
%The online loop:
for k = 2:50
% At time k receive y,u
[th,yh,P,phi] = rarx([y u],[2 2 1],'ff',0.98,th',P,phi);
plot(k,th(1),'∗')
end
Execute iddemo #10 to illustrate the recursive algorithms.
Segmentation of Data
Sometimes the system or signal exhibits abrupt changes during the time when
the data is collected. It may be important in certain applications to find the
time instants when the changes occur and to develop models for the different
segments during which the system does not change. This is the segmentation
problem. Fault detection in systems and detection of trend breaks in time
series can serve as two examples of typical problems.
The System Identification Toolbox offers the function segment to deal with the
segmentation problem. The basic syntax is
thm = segment(z,nn)
with a format like rarx or rarmax. The matrix thm contains the piecewise
constant models in the same format as for the algorithms described earlier in
this section.
The algorithm that is implemented in segment is based on a model description
like (3-58), where the change term w ( t ) is zero most of the time, but now and
then it abruptly changes the system parameters θ 0 ( t ) . Several Kalman filters
that estimate these parameters are run in parallel, each of them corresponding
to a particular assumption about when the system actually changed. The
relative reliability of these assumed system behaviors is constantly judged, and
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unlikely hypotheses are replaced by new ones. Optional arguments allow the
specification of the measurement noise variance R 2 in (3-57), of the probability
of a jump, of the number of parallel models in use, and also of the guaranteed
lifespan of each hypothesis. See segment in the “Command Reference” chapter.
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Some Special Topics
Some Special Topics
This section describes a number of miscellaneous topics. Most of the
information here is also covered in other parts of the manual, but since
manuals seldom are read from the beginning, you can also check if a particular
topic is brought up here.
Time Series Modeling
When there is no input present, the general model (3-43) reduces to the ARMA
model structure.
A ( q )y ( t ) = C ( q )e ( t )
With C ( q ) = 1 you have an AR model structure.
Similarly, a state-space model for a time series is given by
x ( t + 1 ) = Ax ( t ) + Ke ( t )
y ( t ) = Cx ( t ) + e ( t )
so that the matrices B and D are empty.
Basically all commands still apply to these time-series models, but with
natural modifications. They are listed as follows.
m= idpoly(A,[ ],C)
e = iddata([],idinput(300,'rgs'))
y = sim(m,e)
Spectral analysis (etfe and spa) returns results in the idfrd model format,
that now just contains SpectrumData and its variance. bode will only plot these
signal spectra and, if required, the confidence intervals.
g = spa(y)
p= etfe(y)
bode(g,p,'sd',3)
Note that etfe gives the periodogram estimate p of the spectrum.
armax and arx work the same way, but need no specification of nb and nk.
th = arx(y,na)
th = armax(y,[na nc])
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Note that arx also handles multivariable signals, and so do n4sid and pem.
m = n4sid(y) %default order
bode(m)
compare(y,m,10) % 10-step ahead predictions being evaluated.
Structured state-space models of time series can be built simply by specifying
B = [], D = [] in idss and idgrey. resid works the same way for time series
models, but does not provide any input-residual correlation plots.
resid(m,y)
In addition there are two commands that are specifically constructed for
building scalar AR models of time series. One is
m = ar(y,na)
which has an option that allows you to choose the algorithm from a group of
several popular techniques for computing the least squares AR model. Among
these are Burg’s method, a geometric lattice method, the Yule-Walker
approach, and a modified covariance method. See the “Command Reference”
chapter for details. The other command is
m = ivar(y,na)
which uses an instrumental variables technique to compute the AR part of a
time series.
Finally, when no input is present, the functions bj, iv, iv4, and oe are not of
interest.
Here is an example where you can simulate a time series, compare spectral
estimates and covariance function estimates, and also the predictions of the
model.
ts0 = idpoly([1 -1.5 0.7],[]);
ir = sim(ts0,[1;zeros(24,1)]);
Ry0 = conv(ir,ir(25:-1:1)); % The true covariance function
e = idinput(200,'rgs');
y = sim(ts0,e);
plot(y)
per = etfe(y);
speh = spa(y);
ffplot(per,speh,ts0)
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Some Special Topics
ts2 = ar(y,2); % A second order AR model:
ffplot(speh,ts2,ts0,'sd',3)
% The covariance function estimates:
Ryh = covf(y,25);
Ryh = [Ryh(end:-1:2),Ryh]';
ir2 = sim(ts2,[1;zeros(24,1)]);
Ry2 = conv(ir2,ir2(25:-1:1));
plot([-24:24]'∗ones(1,3),[Ryh,Ry2,Ry0])
% The prediction ability of the model:
compare(y,ts2,5)
Periodic Inputs
It is often an advantage to use a periodic input for identification, whenever
possible. See Section 13.3 in Ljung(1999). If you import or create a periodic
input, as in
u = idinput([300 2 5]) % Period 300, 2 inputs, 5 periods
you should set the corresponding period in the iddata object.
u = iddata([],u,'Period',[300; 300]);
Normally, an even number of periods should be represented in the data. That
will allow the estimation routines to do the right things. For example, etfe
when called with data with periodic inputs, will honor the period and compute
the frequency response on a suitably chosen frequency grid. Try this.
m0 =idpoly([1 -1.5 0.7],[0 1 0.5]);
u = idinput([10 1 150],'rbs');
u = iddata([],u,'Period',10);
e = iddata([],randn(1500,1));
y = sim(m0, [u e])
g = etfe([y u])
bode(g,'x',m0) % Good fit at the 5 excited frequencies
Connections Between the Control System Toolbox
and the System Identification Toolbox
The objects and functions of the Control System Toolbox, are quite similar to
those of the System Identification Toolbox. This means that the two toolboxes
can be run efficiently together.
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Function Calls
The function calls are the same for many essential functions. bode, nyquist,
step, impulse, ssdata, tfdata, zpkdata, freqresp, minreal, etc., all do the
same things with essentially the same syntax. The System Identification
Toolbox commands however also handle model uncertainty. The System
Identification Toolbox commands will be used whenever at least one of the
objects in the argument list is an idmodel or idfrd object.
Also, subreferencing channels and concatenations follow the same syntax.
Moreover, most of the LTI-commands for model manipulation, like G1+G2,
G1*G2, feedback, append, balreal, augstate, canon, etc, will work (using the
Control System Toolbox) in the expected way, returning idmodel objects.
However, covariance information is in most cases lost.
Object Relations
Since the System Identification Toolbox can be run without the Control System
Toolbox, there are no formal parent/child relations between the objects in the
two toolboxes. There are however easy transformations between them. The
command that creates idmodel, idss, and idpoly will accept any LTI object,
zpk, tf or ss. idfrd can similarly be created from frd objects. If the LTI object
has an InputGroup named 'noise' these input will be treated as normalized
white noise, when creating the idmodel object with correct disturbance model
information.
Analogously, ss, zpk, tf, and frd accept any idmodel or idfrd (in case of frd)
object. The covariance information is then not stored in the LTI objects, but all
disturbance information will be translated to a group of extra input channels
with the group name 'noise'. If these are interpreted as normalized white
noise, the LTI objects have the same disturbance properties as the original
imdmodel object.
These simple relations also mean that it is easy to use any LTI command in the
Control System Toolbox and return to System Identification Toolbox objects.
Mb = idss(balreal(ss(M)))
Plot Relations
Although the calls bode, step etc., have essentially the same syntax, the plots
look different. The System Identification Toolbox commands show, when
required, confidence regions, and typically show the different input/output
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Some Special Topics
channels as separate plots. The sorting of the channels is based on the
InputName and OutputName properties. Therefore the System Identification
Toolbox commands allow any mix of models not necessarily of the same sizes.
The System Identification Toolbox plot commands do not offer the same options
and plot interaction facilities as ltiview. However, applying view to one or
several idmodel objects invokes the LTI Viewer.
Here is an example of the interplay between the functions in the two toolboxes.
m0 = drss(4,3,2)
m0 = idss(m0,'NoiseVar',0.1*eye(3))
u = iddata([], idinput([800 2],'rbs'));
e = iddata([], randn(800, 3));
y = sim(m0, [u e])
Data = [y u];
m = pem(Data(1:400))
tf(m)
compare(Data(401:800),m)
view(m)
Memory - Speed Trade-Offs
On machines with no formal memory limitations, it is still of interest to
monitor the sizes of the matrices that are formed. The typical situation is when
an overdetermined set of linear equations is solved for the least squares
solution. The solution time depends, of course, on the dimensions of the
corresponding matrix. The number of rows corresponds to the number of
observed data, while the number of columns corresponds to the number of
estimated parameters. The property MaxSize used with all the relevant
M-files, guarantees that no matrix with more than MaxSize elements is
formed. Larger data sets and/or higher order models are handled by for loops.
for loops give linear increase in time when the data record is increased, plus
some overhead.
If you regularly work with large data sets and/or high order models, it is
advisable to tailor the memory and speed trade-off to your machine by choosing
MaxSize carefully. You could also change the default value of MaxSize in the
M-file idmsize. Then the default value of MaxSize (that is ’Auto') will be
tailored to your needs. Note that this value is allowed to depend on the number
of rows and columns of the matrices formed.
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Local Minima
The iterative search procedures in pem, armax, oe, and bj lead to models
corresponding to a local minimum of the criterion function (3-39). Nothing
guarantees that this local minimum is also a global minimum. The start-up
procedure for black-box models in these routines is, however, reasonably
efficient in giving initial estimates that lead to the global minimum.
If there is an indication that a minimum is not as good as you expected, try
starting the minimization at several different initial conditions, to see if a
smaller value of the loss function can be found. The function init can be used
for that.
Initial Parameter Values
When only orders and delays are specified, the functions armax, bj, oe, and pem
use a start-up procedure to produce initial values. The start-up procedure goes
through two to four least squares and instrumental variables steps. It is
reasonably efficient in that it usually saves several iterations in the
minimization phase. Sometimes it may, however, pay to use other initial
conditions. For example, you can use an iv4 estimate computed earlier as an
initial condition for estimating an output-error model of the same structure.
m1 = iv4(Data,[na nb nk]);
set(m1,'a',1,'f',m1.a)
m2= oe(Data,m1);
Another example is when you want to try a model with one more delay (for
example, three instead of two) because the leading b-coefficient is quite small.
m1 = armax(Data,[3 3 2 2]);
m1.b(3) = 0
m2 = armax(Data,m1);
If you decrease the number of delays, remember that leading zeros in the
B-polynomial are treated as delays. Suppose you go from three to two delays
in the above example.
m1 = armax(z,[3 3 2 3]);
m1.b(3) = 0.00001;
m2 = armax(Data,m1);
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Some Special Topics
Note that when constructing homemade initial conditions, the conditions must
correspond to a stable predictor (C and F being Hurwitz polynomials), and they
should not contain any exact pole-zero cancellations.
For user defined structured state-space and multi-output models, you must
provide the initial parameter values (initial model) when defining the
structure in idss or idgrey. The basic approach is to use physical insight to
choose initial values of the parameters with physical significance, and try some
different (randomized) initial values for the others. The routine init can be
used for that.
Initial State
The filter that computes the prediction errors in (3-36) needs to be properly
initialized. For input-output (polynomial) models, values of inputs, outputs and
predictions prior to time t = 0 are required, and state-space models need the
initial state x(0). There are several ways to handle these unknown states. A
simple one is to take all unknown values as zero. If the model predictor has
slow dynamics (i.e. the poles of CF, or the eigenvalues of A-KC are close to the
unit circle), this could have a very bad effect on the parameter estimates. It is
particularly pronounced for output-error models, where the noise model cannot
be adjusted to handle slow transients form initial conditions.
The toolbox offers a number of options how to deal with the initial state of the
predictor. They are handled by the model property InitialState. The
unknown state can be treated as a vector of unknown parameters
(InitialState = 'Estimate'), they can be set to zero (InitialState =
'Zero'), or estimated by a backwards prediction method (InitialState =
'Backcast'). It can also be fixed to any user defined value. The default value
is InitialState = 'Auto', which makes an automatic choice between the
options, guided by the estimation data. For details, see idss and idpoly in the
“Command Reference” chapter. Basically, the effect of the initial conditions on
the prediction errors are tested and if they seem to be negligible, 'zero' is
chosen, which gives a fast and efficient algorithm. Otherwise the initial state
is estimated or “backcasted.” EstimationInfo will contain information about
which method was chosen in this case.
Proper handling of the initial state is necessary both when models are
estimated, and when predictions and simulations are compared. The
commands predict, pe, sim, and compare all offer options for how to deal with
this.
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The Estimated Parameter Covariance Matrix
The estimated parameters are uncertain. The amount of uncertainty is
measured and described by the covariance matrix of the estimated parameter
vector, (this vector is a random variable, since it depends on the random noise
that has affected the output). This covariance (uncertainty) can also be
estimated from data, as described, e.g. in Chapter 9 of Ljung (1999). The
estimated covariance matrix is contained in the estimated model as the
property Model.CovarianceMatrix. It is used to compute all relevant
uncertainty measures of various model input-output properties (Bode plots,
uncertain model output, zeros and poles, etc.)
The estimate of the covariance matrix is based on the assumption that the
model structure is capable of giving a correct description of the system. For
models that contain a disturbance model (H is estimated) it, thus, assumed
that the model will produce white residuals, for the uncertainty estimate to be
correct.
However, for output-error models (H fixed to 1, corresponding to K = 0 for state
space models, and C = D = A = 1 for polynomial models), it is not assumed that
the residuals are white. Instead, their color is estimated and a correct estimate
of the covariance estimate is used. This corresponds to eq (9.42) in Ljung
(1999).
No Covariance
Evaluating and visualizing the uncertainty of the estimated models is a very
important aspect of system identification. Handling, and translating
covariance information takes a major part of the time in many of the routines
of the System Identification Toolbox. For example, in n4sid, calculating the
Cramer-Rao bound (which in this case is used and an indication of the
covariance properties) takes much longer than estimating the actual model. In
d2c and c2d, most of the time is spent on covariance handling. If you build
models that are of a preliminary nature, and you would like to speed up the
calculations, you can add the property name/property value pair
'Covariance'/'None' to the list of arguments in most relevant routines. This
will prevent covariance calculations and set a flag not to spend time on this in
future use of the model. This flag can also be set in the model at any time by
Model.cov = 'no'
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nk and InputDelay
What’s the difference between the properties nk and InputDelay? InputDelay
is defined for all idmodel and idfrd objects, while nk is defined for idarx,
idpoly as well as for 'Free' and 'Canonical' idss models. Both properties
indicate a delay from the input channels to the outputs. For idarx, nk is a
matrix, describing the delays in the different input/output channels, but
otherwise both nk and InputDelay describe the delay from a certain input
channel to all the output channels.
InputDelay is really a flag that tells the model to append the input delays as
time lags, when the model is simulated, or as phase lags when the frequency
functions are computed. The InputDelay does not show up when the model is
represented in state-space form, nor as transfer functions, nor in the
input-output polynomials. InputDelay can be used both for continuous and
discrete time models. In the latter case, the InputDelay is measured in number
of samples. Moreover InputDelay may assume negative values, in order to
handle noncausal models.
The property nk, on the other hand, is a model structure property, requiring the
model to contain the indicated number of delays whatever the parameter
values. This means that the state-space matrices, the transfer functions, etc.,
will show these delays in an explicit manner. Consequently, nk is not defined
for continuous-time models.
Otherwise the two properties can be used in the same way
m1 = pem(Data,4,'InputDelay',[3 2 0])
m2 = pem(Data,4,'nk',[3 2 0])
bode(m1,m2)
A1 = m1.A
A2 = m2.A
give identical bode-plots (up to minor variations due to end-effects in the data
records), while A1 and A2 are different. In fact while A1 is of size 4-by-4, the
matrix A2 is of size 7-by-7, since three extra states are required to accommodate
the extra 2+1 input delays.
Note that setting nk to a certain value for a given model gives a model structure
that has the indicated delay for any parameter values. The impulse response
of the model may however change (not only be shifted) by this assignment.
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Linear Regression Models
A linear regression model is of the type
T
y(t ) = θ ϕ( t) + e( t)
(3-65)
where y ( t ) and ϕ ( t ) are measured variables and e ( t ) represents noise. Such
models are very useful in most applications. They allow, for example, the
inclusion of nonlinear effects in a simple way. The System Identification
Toolbox function arx allows an arbitrary number of inputs. You can therefore
handle arbitrary linear regression models with arx. For example, if you want
to build a model of the type
2
3
y ( t ) = b0 + b1 u ( t ) + b 2 u ( t ) + b3 u ( t )
(3-66)
let
Data = iddata(y,[ones(size(u)), u, u.^2, u.^3]);
m= arx(Data,'na',0,'nb',[1 1 1 1],'nk',[ 0 0 0 0])
This is formally a model with one output and four inputs, but all the model
testing in terms of compare, sim, and resid operate in the natural way for the
model (3-65), once the data set Data is defined as above.
Note that when pem is applied to linear regression structures, by default a
robustified quadratic criterion is used. The search for a minimum of the
criterion function is carried out by iterative search. Normally, use this
robustified criterion. If you insist on a quadratic criterion, then set the
argument LimitError in pem to zero. Then pem also converges in one step.
Spectrum Normalization and the Sampling Interval
In the function spa the spectrum estimate is normalized with the sampling
interval T as
M
Φy ( ω ) = T
∑
k = –M
where
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R y ( kT )e
– iωT
WM( k )
(3-67)
Some Special Topics
N
1
ˆ ( kT ) = ---R
y
N
∑ y ( lT – kT )y ( lT )
l=1
(See also (3-3)). The normalization in etfe is consistent with (3-67). This
normalization means that the unit of Φ y ( ω ) is “power per radians/time unit”
and that the frequency scale is “radians/time unit.” You then have
π⁄T
1
Ey ( t ) = -----2π
2
∫
Φ y ( ω ) dω
(3-68)
–π ⁄ T
In MATLAB, therefore, you have S1 ≈ S2 where
y.ts
sp =
phiy
S1 =
S2 =
= T
spa(y);
= squeeze(sp.spec) % squeeze takes out the spurios dimensions
sum(phiy)/length(phiy)/T;
sum(y.^2)/size(y,1);
Note that PHIY contains Φ y ( ω ) between ω = 0 and ω = π ⁄ T with a frequency
step of ¼ π / (T length(phiy)). The sum S1 is, therefore, the rectangular
approximation of the integral in (3-68). The spectrum normalization differs
from the one used by spectrum in the Signal Processing Toolbox, and the above
example shows the nature of the difference.
The normalization with T in (3-67) also gives consistent results when time
series are decimated. If the energy above the Nyquist frequency is removed
before decimation (as is done in resample), the spectral estimates coincide;
otherwise you see folding effects.
Try the following sequence of commands.
m0 = idpoly(1,[ ],[1 1 1 1]);
% 4th order MA-process
e = idinput(2000,'rgs')
e = iddata([], e, 'Ts', 1);
y = sim(m0, e);
g1 = spa(y);
g2 = spa(y(1:4:2000)); % This code automatically sets Ts to 4.
ffplot(g1,g2) % Folding effects
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g3 = spa(resample(y,1,4)); % Prefilter applied
ffplot(g1,g3) % No folding
For a parametric noise (time series) model
y ( t ) = H ( q )e ( t );
2
Ee ( t ) = λ
the spectrum is computed as
Φ y ( ω ) = λT H ( e
iωT 2
)
(3-69)
which is consistent with (3-67) and (3-68). Think of λT as the spectral density
of the white noise source e ( t ) .
When a parametric disturbance model is transformed between continuous time
and discrete time and/or resampled at another sampling rate, the functions c2d
and d2c in the System Identification Toolbox use formulas that are formally
correct only for piecewise constant inputs. (See (3-29)). This approximation is
good when T is small compared to the bandwidth of the noise. During these
transformations the variance λ of the innovations e ( t ) is changed so that the
spectral density T . λ remains constant. This has two effects:
• The spectrum scalings are consistent, so that the noise spectrum is
essentially invariant (up to the Nyquist frequency) with respect to
resampling.
• Simulations with noise using sim has a higher noise level when performed at
faster sampling.
This latter effect is well in line with the standard description that the
underlying continuous-time model is subject to continuous-time white noise
disturbances (which have infinite, instantaneous variance), and appropriate
low-pass filtering is applied before sampling the measurements. If this effect is
unwanted in a particular application, scale the noise source appropriately
before applying sim.
Note the following cautions relating to these transformations of disturbance
models. Continuous-time disturbance models must have a white noise
component. Otherwise the underlying state-space model, which is formed and
used in c2d and d2c, is ill-defined. Warnings about this are issued by idpoly
and these functions. Modify the C-polynomial accordingly. Make the degree of
3-96
Some Special Topics
the monic C-polynomial in continuous time equal to the sum of the degrees of
the monic A- and D-polynomials; i.e., in continuous time.
length(C)-1 = (length(A)-1)+(length(D)-1)
Interpretation of the Loss Function
The value of the quadratic loss function is given as the field LossFcn in the
EstimationInfo of the model.
m.es.LossFcn
For multi-output systems, this is equal to the determinant of the estimated
covariance matrix of the noise source e
For most models the estimated covariance matrix of the innovations is obtained
by forming the corresponding sample mean of the prediction errors (squared),
computed (using pe) from the model with the data for which the model was
estimated.
Note the discrepancy between this value and the values shown during the
minimization procedure (in pem, armax, bj, or oe), since these are the values of
the robustified loss function (see under LimitError on page 3-33). Note also
that it is the non-robustified residuals that are used to estimate the variance
of e, as stored in Model.NoiseCovariance. It is also this value that is used to
estimate the covariance matrix of the estimated parameters. Outliers may thus
influence the estimate of NoiseVariance and the covariance matrix, while the
parameter estimates are made robust against them.
Be careful when comparing loss function values between different structures
that use very different disturbance models. An Output-Error model may have
a better input-output fit, even though it displays a higher value of the loss
function than, say, an ARX model.
Note that for ARX models computed using iv4, the covariance matrix of the
innovations is estimated using the provisional disturbance model that is used
to form the optimal instruments. The loss function therefore differs from what
would be obtained if you computed the prediction errors using the model
directly from the data. It is still the best available estimate of the innovations
covariance. In particular, it is difficult to compare the loss function in an ARX
model estimated using arx and one estimated using iv4.
3-97
3
Tutorial
Enumeration of Estimated Parameters
In some cases the parameters of a model are given just as an ordered list. This
is the case for m.ParameterVector and also when online information from the
minimization routines are given:
• For the input-output model (3-19) or its multi-input variant (3-41), you have
the following alphabetical ordering.
1
1
2
2
pars = [ a 1, …, a na, b 1, …, b nb1, b 1, …b nb2, …
nu
nu
b 1 , …, b nbnu, c 1, …, c nc, d 1, …, d nc,
1
1
nu
nu
f 1, …f nf1, …, f 1 , …, f nfnu ]
Here superscript refers to the input number:
• For a state-space structure, defined by idss the parameters in
m.ParameterValues are obtained in the following order. The A matrix is
first scanned row by row for free parameters. Then the B matrix is scanned
row by row, and so on for the C, D, K, and X0 matrices.
• For a state-space matrix that is defined by idgrey, the ordering of the
parameters is the same as in the user-written M-file.
Multivariable ARX models are internally represented in state-space form. The
parameter ordering follows the one described above. The ordering of the
parameters may, however, not be transparent so it is better to use idarx and
arxdata.
Note that the property PName (for Parameter Name) may be useful to help with
the bookkeeping in these cases, and when fixing certain parameters using
FixedParameter. The routine setpname may be helpful in automatically setting
mnemonic parameter names for black-box models.
Complex-Valued Data
Some applications of system identification work with complex-valued data, and
thus create complex-valued models. Most of the routines in the System
Identification Toolbox support complex data and models. This is true for the
estimation routines ar, armax, arx, bj, covf, ivar, iv4, oe, pem, spa, and n4sid.
3-98
Some Special Topics
The transformation routines, like freqresp, zpkdata, etc., also work for
complex-valued models, but no pole-zero confidence regions are given. Note
also that the parameter variance-covariance information then refers to the
complex valued parameters, so no separate information about the accuracy of
the real and imaginary parts will be given. Some display functions like compare
and plot do not work for the complex case. Use sim and plot real and
imaginary parts separately.
Strange Results
Strange results can of course be obtained in any number of ways. We only
point out two cautions: It is tempting in identification applications to call the
residuals eps. Don’t do that. This changes the machine ε , which certainly will
give you strange results.
It is also natural to use names like step, phase, etc., for certain variables. Note
though that these variables take precedence over M-files with the same name
so be sure you don’t use variable names that also are names of M-files.
3-99
3
Tutorial
3-100
4
Command Reference
4
Command Reference
This chapter contains detailed descriptions of all of the functions in the System
Identification Toolbox. It begins with a list of functions grouped by subject area
and continues with the entries in alphabetical order. Information is also
available through the online Help facility.
By typing a function name without arguments, you also get immediate syntax
help about its arguments.
For ease of use, most functions have several default arguments. The Synopsis
first lists the function with the necessary input arguments and then with all
the possible input arguments. The functions can be used with any number of
arguments between these extremes. The rule is that missing, trailing
arguments are given default values, as defined in the manual. Default values
are also obtained by entering the arguments as the empty matrix [ ].
MATLAB does not require that you specify all of the output arguments; those
not specified are not returned. For functions with several output arguments in
the System Identification Toolbox, missing arguments are, as a rule, not.
Help Functions
help ident
List the commands.
idhelp
A micro-manual.
idprops,
help idprops
List and explain the object properties.
The Graphical User Interface
4-2
ident
Open the interface.
midprefs
Set directory where to store start-up information.
Simulation and Prediction
idinput
Generate input signals.
pe
Compute prediction errors.
predict
Compute predictions according to model.
sim
Simulate a general linear system.
Data Manipulation
detrend
Remove trends from data.
get/set
Retrieve and modify iddata properties.
iddata
Construct a data object.
idfilt
Filter data.
merge (iddata)
Merge data sets into a multiple experiment set.
misdata
Reconstruct missing input and output data.
plot (iddata)
Plot data.
resample
Resample data.
Nonparametric Estimation
covf
Estimate covariance function.
cra
Estimate impulse response and covariance functions
using correlation analysis.
impulse, step
Estimate impulse and step responses using high
order parametric models.
etfe
Estimate spectra and transfer functions using direct
Fourier techniques.
spa
Estimate spectra and transfer functions using
spectral analysis.
4-3
4
Command Reference
Parameter Estimation
ar
Estimate AR model.
armax
Estimate ARMAX model.
arx
Estimate ARX model using least squares.
bj
Estimate Box-Jenkins model.
ivar
Estimate AR model using instrumental variable
methods.
iv4
Estimate ARX model using four-stage instrumental
variable method.
oe
Estimate Output-Error model.
n4sid
Estimate state-space model using subspace method.
pem
Estimate general linear model.
Model Structure Creation
4-4
idarx
Create multivariable ARX-models.
idfrd
Create Identified Frequency Response Data object.
idgrey
Create a greybox linear model using an M-file that
you write.
idpoly
Create a model structure for input-output models
defined as numerator and denominator polynomials.
idss
Create model structure for linear state-space models
with known and unknown parameters.
Manipulating Model Structures
get/set
Retrieve and modify model structures.
init
Select or randomize initial parameter values.
merge (idmodel)
Merge estimated models.
Model Conversions
arxdata
Compute ARX parameters.
idmodred
Reduce a model to lower order.
c2d
Transform from continuous to discrete time.
d2c
Transform from discrete to continuous time.
freqresp
Compute frequency response.
idfrd
Convert idmodel to the IDFRD object that contains
frequency functions and spectra.
noisecnv
Convert noise inputs to measured channels
polydata
Compute transfer function polynomials.
ssdata
Compute state-space matrices.
tfdata
Compute transfer functions.
ss, tf, zpk, frd
Conversion of idmodel to the LTI-objects of the
Control Systems Toolbox.
zpkdata
Compute zeros, poles, and gains.
4-5
4
Command Reference
Model Analysis
bode
Plot Bode diagrams.
compare
Compare measured and simulated outputs.
ffplot
Plot frequency functions and spectra.
impulse, step
Plot impulse and step responses.
nyquist
Plot Nyquist diagrams.
present
Display model on screen.
pzmap
Plot zeros and poles.
view
Plot model characteristics using the LTI Viewer in
the Control System Toolbox.
Model Validation
4-6
aic, fpe
Compute model selection criteria.
arxstruc, selstruc
Select ARX-structure.
compare
Compare model’s simulated or predicted output with
actual output.
pe
Compute prediction errors.
predict
Predict future outputs.
resid
Compute and test model residuals.
sim
Simulate a model.
Assessing Model Uncertainty
simsd
Simulate responses from several possible models.
bode, nyquist
Frequency responses with confidence regions.
impulse, step, sim
Time responses with confidence regions.
pzmap
Pole/zero plot with confidence regions.
arxdata, polydata,
ssdata, tfdata,
zpkdata
Model data with variance information.
Model Structure Selection
arxstruc
Compute loss functions for sets of ARX model
structure.
ivstruc
Compute loss functions for sets of output error model
structures.
n4sid, pem
State-space model order can be give as a range.
selstruc
Select structure.
struc
Generate sets of structures.
4-7
4
Command Reference
Recursive Parameter Estimation
rarmax
Estimate ARMAX or ARMA models recursively.
rarx
Estimate ARX or AR models recursively.
rbj
Estimate Box-Jenkins models recursively.
roe
Estimate Output-Error models (IIR-filters)
recursively.
rpem
Estimate general input-output models using a
recursive prediction error method.
rplr
Estimate general input-output models using a
recursive pseudo-linear regression method.
segment
Segment data and estimate models for each segment.
General
4-8
get
Retrieve object properties.
set
Set object properties.
setpname
Set default, mnemonic parameter names.
size
Give sizes of the different objects.
timestamp
Show object’s time of creation.
aic
Purpose
4aic
Compute the Akaike Information Criterion for an estimated model.
Syntax
am = aic(Model)
Description
Model is any estimated idmodel (idarx, idgrey, idpoly, idss).
am is returned as the value of Akaike’s Information Criterion
2d
AIC = log ( V ) + ------N
where V is the loss function, d is the number of estimated parameters, and N
is the number of estimation data.
See Also
EstimationInfo, fpe
Reference
Sections 7.4 and 16.4 in Ljung (1999)
4-9
Algorithm Properties
Purpose
4Algorithm Properties
Describe the algorithm properties that affect the estimation process.
Syntax
idprops algorithm
m.algorithm
Description
All the idmodel objects in the toolbox, idarx, idss, idpoly, and idgrey, have a
property Algorithm, which is a structure that contains a number of options
that govern the estimation algorithms. The fields of this structure can be
individually set and retrieved in the usual way, such as get(m,'MaxIter') or
m.SearchDirection = 'gn'. Also, autofill applies and the names are case
insensitive.
Note 1 Algorithm is a property of idmodel. Any algorithm property can be
separately set as above. Also, if you have a standard algorithm setup that you
prefer, you can set those properties simultaneously as in
m = pem(Data,mi,'alg',myalg).
Note 2 The algorithm properties, like all other model properties, will be
inherited by the resulting model m. If you continue the estimation using m as
initial model, all previously set algorithm features will thus apply, unless you
specify otherwise.
The fields of Algorithm are as follows:
Applying to all estimation methods
• Focus: This property affects the weighting applied to the fit between the
model and the data. It can be used to assure that the model approximates the
true system well over certain frequency intervals. Focus can assume the
following values:
- 'Prediction': This is the default and means that the model is determined
by minimizing the prediction errors. It corresponds to a frequency
weighting that is given by the input spectrum times the inverse noise
model. Typically, this favors a good fit at high frequencies. From a
4-10
Algorithm Properties
statistical variance point of view, this is the optimal weighting, but then
the approximation aspects (bias) of the fit are neglected.
- 'Simulation': This means that frequency weighting of the transfer
function fit is given by the input spectrum. Frequency ranges where the
input has considerable power will thus be better described by the model.
In other words, the model approximation is such that the model will
produce as good simulations as possible, when applied to inputs with the
same spectra as used for the estimation. For models that have no
disturbance model, that is y = G u + e, (A=C=D=1 for idpoly models and K=0
for idss models) there is no difference between 'Simulation' and
'Prediction'. For models with a disturbance description, i.e. y = Gu + H
e, G is first estimated with H = 1 and then H is estimated by a prediction
ˆ fixed. This
error method, keeping the estimated transfer function G
option will also guarantee a stable transfer function G.
- ’Stability’: The resulting model is guaranteed to be stable, but a
prediction weighing is still maintained. Note that forcing the model to be
stable could mean that a bad model is obtained. Use only when you know
the system to be stable.
- Any SISO linear filter. Then the transfer function from input to output is
determined by a frequency fit with this filter times the input spectrum as
weighting function. The disturbance model is determined by a prediction
error method, keeping the transfer function estimate fixed, as in the
simulation case. To obtain a good model fit over a special frequency range,
the filter should thus be chosen with a passband over this range. For a
model with no disturbance model, the result is the same as first applying
prefiltering to data using idfilt. The filter can be specified in a few
different ways as:
- Any single-input-single-output idmodel
- An ss, tf or zpk model from the Control System Toolbox
- {A,B,C,D} with the state-space matrices for the filter
- {numerator, denominator} with the transfer function numerator/
denominator of the filter
• MaxSize: No matrix with more than MaxSize elements is formed by the
algorithm. Instead, for-loops will be used. MaxSize will thus decide the
memory/speed trade-off, and can prevent slow use of virtual memory.
MaxSize can be any positive integer, but it is required that the input-output
4-11
Algorithm Properties
data contain less than MaxSize elements. The default value of MaxSize is
'Auto', which means that the value is determined in the M-file idmsize. You
can edit this file to optimize speed on a particular computer.
• FixedParameter: A list of parameters that will be kept fixed to the nominal/
initial values and not estimated. This is a vector of integers containing the
indices of the fixed parameters. The numbering of the parameters is the
same as in the model property 'ParameterVector'. The parameter names
from the property 'PName' can also be used. For structured state-space
models, it is easier to fix/unfix parameters by the structure matrices, As, Bs,
etc. See idss. When using parameter names to specify the fixed parameters,
Fixedparameter is a cell array of strings. The strings may contain the
wildcards ‘*’ (meaning any continuation of the given string) and ‘?’ (meaning
any character). For example, if all disturbance model parameters start with
‘k’, FixedParameter = {'k*'} will fix all these parameters. The function
setpname may be useful in this context.
Applying to n4sid, estimating state-space models
These also apply to pem for estimating black-box state-space models, since
these are initialized by the n4sid estimate.
• N4Weight: This property determines some weighting matrices used in the
singular-value decomposition that is a central step in the algorithm. Two
choices are offered: 'MOESP', which corresponds to the MOESP algorithm by
Verhaegen, and 'CVA', which is the canonical variable algorithm by
Larimore. See the reference page for n4sid. The default value is 'N4Weight'
= 'Auto', which gives an automatic choice between the two options.
• N4Horison: Determines the prediction horizons forward and backward, used
by the algorithm. This is a row vector with three elements:
N4Horison =[r sy su], where r is the maximum forward prediction horizon,
i.e., the algorithms uses up to r-step ahead predictors. sy is the number of
past outputs, and su is the number of past inputs that are used for the
predictions. For an exact definition of these integers, see pages 209-210 in
Ljung(1999), where they are called r, s1 and s2. These numbers may have a
substantial influence of the quality of the resulting model, and there are no
simple rules for choosing them. Making 'N4Horizon' a k-by-3 matrix means
that each row of 'N4Horison' will be tried out, and the value that gives the
best (prediction) fit to data will be selected. (This option cannot be combined
with selection of model order.) If you specify only one column in 'N4Horizon',
4-12
Algorithm Properties
the interpretation is r=sy=su. The default choice is 'N4Horizon' = 'Auto',
which uses an Akaike Information Criterion (AIC) for the selection of sy and
su.
Applying to estimation methods using iterative search for minimizing a
criterion, i.e., armax, bj, oe, and pem
• Trace: This property determines the information about the iterative search
that is provided to the MATLAB command window.
- 'Trace' = 'Off': No information is written to the screen.
- 'Trace' = 'On': Information about criterion values and the search
process is given for each iteration.
- 'Trace'= 'Full': The current parameter values and the search direction
are also given (except in the 'Free' SSParameterization case for idss
models)
• LimitError: This variable determines how the criterion is modified from
quadratic to one that gives linear weight to large errors. Errors larger than
LimitError times the estimated standard deviation will carry a linear
weight in the criterions.The default value of LimitError is 1.6. LimitError
= 0 disables the robustification and leads to a purely quadratic criterion.
The standard deviation is estimated robustly as the median of the absolute
deviations from the median, divided by 0.7. (See Eq. (15.9)-(15.10) in Ljung
(1999)).
• MaxIter: The maximum number of iterations performed during the search
for minimum. The iterations will stop when MaxIter is reached, or some
other stopping criterion is satisfied. The default value of MaxIter is 20.
Setting MaxIter = 0 will return the result of the start-up procedure. The
actual number of used iterations is given by the property
EstimationInfo.Iterations.
• Tolerance: Based on the Gauss-Newton vector computed at the current
parameter value, an estimate is made of the expected improvement of the
criterion at the next iteration. When this expected improvement is less than
Tolerance, measured in percent, the iterations are stopped. Default value:
0.01.
• SearchDirection: The direction along which a line search is performed to
find a lower value of the criterion function. It may assume the following
values:
4-13
Algorithm Properties
- 'gn': The Gauss-Newton direction (inverse of the Hessian times the
gradient direction). If no improvement is found along this direction, the
gradient direction is also tried out.
- 'gns': A regularized version of the Gauss-Newton direction. Eigenvalues
less than GnsPinvTol (see “Advanced” below) of the Hessian are neglected,
and the Gauss-Newton direction is computed in the remaining subspace.
- 'lm': The Levenberg-Marquard method is used. This means that the next
parameter value is -pinv(H+d*I)*grad from the previous one, where H is
the Hessian, I is the identity matrix, grad is the gradient. d is a number
that is increased until a lower value of the criterion is found.
- 'Auto': A choice between the above is made in the algorithm. This is the
default choice.
• Advanced: This is a structure that contains detailed algorithm choices, that
normally the user does not need to get involved in. For detailed explanations,
the code will have to be examined. 'Advanced' has the following fields:
- Search: Contains fields with relevance for the iterative search:
a GnsPinvTol: The tolerance for the pseudoinverse used to compute the gns
direction. See above. Default 10^-9.
b LmStep: The next value of d in the LM method is lmstep times the
previous one. Default lmstep = 2.
c
StepReduction: In the line search used for other directions than LM, the
step is reduced by the factor stepred in each try. Default:
StepReduction = 2.
d MaxBisection: The maximum number of bisections used by the line
search along the search direction. Default 10.
e LmStartValue: The starting value of d in the LM method. Default 0.001.
f
RelImprovement: The iterations are stopped if the relative improvement
of the criterion is less than relimp. Default RelImprovement= 0.
- Threshold: Contains fields with thresholds for several tests:
a Sstability: used for stability test of continuous time models. Model is
considered stable if its rightmost pole is to the left of Sstability. Default
0.
b Zstability: used for stability test of discrete time models. Model is
considered stable if all poles are within the distance Zstability from the
origin. Default 1.01.
4-14
Algorithm Properties
- AutoInitialState: When InitialState = ’Auto’, the state will be
estimated if the ratio of the prediction error norm with zero initial state,
to the norm with estimated initial state exceeds AutoInitialState.
Default 1.2.
See Also
armax, bj, EstimationInfo, n4sid, oe, pem
Reference
For the iterative minimization, see Dennis, J.E. Jr. and R.B. Schnabel.
Numerical Methods for Unconstrained Optimization and Nonlinear Equations,
Prentice Hall, Englewood Cliffs, N.J. 1983.
For a general reference to the identification algorithms, see Ljung (1999),
Chapter 10.
4-15
ar
Purpose
4ar
Estimate the parameters of an AR model for scalar time series.
Syntax
m = ar(y,n)
[m ,refl] = ar(y,n,approach,window,maxsize)
Description
The parameters of the AR model structure
A ( q )y ( t ) = e ( t )
are estimated using variants of the least-squares method.
The iddata object y contains the time-series data (just one output channel).
The scalar n specifies the order of the model to be estimated (the number of A
parameters in the AR model).
Note that the routine is for scalar time series only. For multivariate data use
arx.
The estimate is returned in m and stored as an idpoly model. For the two
lattice-based approaches, 'burg' and 'gl' (see below), the variable refl is
returned containing the reflection coefficients in the first row, and the
corresponding loss function values in the second. The first column is the zero-th
order model, so that the (2,1) element of refl is the norm of the time series
itself.
Variable approach allows you to choose an algorithm from a group of several
popular techniques for computing the least-squares AR model. Available
methods are as follows:
approach = 'fb': The forward-backward approach. This is the default
approach. The sum of a least-squares criterion for a forward model and the
analogous criterion for a time-reversed model is minimized.
approach = 'ls': The least-squares approach. The standard sum of squared
forward prediction errors is minimized.
approach = 'yw': The Yule-Walker approach. The Yule-Walker equations,
formed from sample covariances, are solved.
approach = 'burg': Burg’s lattice-based method. The lattice filter equations
are solved, using the harmonic mean of forward and backward squared
prediction errors.
4-16
ar
approach = 'gl': A geometric lattice approach. As in Burg’s method, but the
geometric mean is used instead of the harmonic one.
The computation of the covariance matrix can be suppressed in any of the
above methods by ending the approach argument with 0 (zero), for example,
'burg0'.
Windowing, within the context of AR modeling, is a technique for dealing with
the fact that information about past and future data is lacking. There are a
number of variants available:
window = 'now': No windowing. This is the default value, except when
approach = 'yw'. Only actually measured data are used to form the regression
vectors. The summation in the criteria starts only at time n.
window = 'prw': Pre-windowing. Missing past data are replaced by zeros, so
that the summation in the criteria can be started at time zero.
window = 'pow': Post-windowing. Missing end data are replaced by zeros, so
that the summation can be extended to time N + n. (N being the number of
observations.)
window = 'ppw': Pre- and post-windowing. This is used in the Yule-Walker
approach.
The combinations of approaches and windowing have a variety of names. The
least-squares approach with no windowing is also known as the covariance
method. This is the same method that is used in the arx routine. The MATLAB
default method, forward-backward with no windowing, is often called the
modified covariance method. The Yule-Walker approach, least-squares plus
pre- and post-windowing, is also known as the correlation method.
See Algorithm Properties for an explanation of the input argument maxsize.
Examples
Compare the spectral estimates of Burg’s method with those found from the
forward-backward nonwindowed method, given a sinusoid in noise signal.
y = sin([1:300]') + 0.5*randn(300,1);
y = iddata(y);
mb = ar(y,4,'burg');
mfb = ar(y,4);
bode(mb,mfb)
4-17
ar
See Also
arx, etfe, ivar, spa
References
Marple, Jr., S. L. Digital Spectral Analysis with Applications, Prentice Hall,
Englewood Cliffs, 1987, Chapter 8.
4-18
armax
Purpose
4armax
Estimate the parameters of an ARMAX or ARMA model.
Syntax
m = armax(data,orders)
m = armax(data,'na',na,'nb',nb,'nc',nc,'nk',nk)
m = armax(data,orders,'Property1',Value1,...,'PropertyN',ValueN)
Description
armax returns m as an idpoly object with the resulting parameter estimates,
together with estimated covariances.
armax estimates the parameters of the ARMAX model structure
A ( q )y ( t ) = B ( q )u ( t – nk ) + C ( q )e ( t )
using a prediction error method.
data is an iddata object containing the output-input data. The model orders
can be specified as (...,'na',na,'nb',nb,...) or by setting the argument
orders to
orders = [na nb nc nk]
The parameters na, nb, and nc are the orders of the ARMAX model, and nk is
the delay. Specifically,
–1
na:
A ( q ) = 1 + a1 q
nb:
B ( q ) = b1 + b2 q
nc:
C ( q ) = 1 + c1 q
+ … + a na q
–1
–1
– na
+ … + b nb q
+ … + c nc q
– nb + 1
– nc
Alternatively, you can specify the vector as
orders = mi
where mi is an initial guess at the ARMAX model given in idpoly format. See
“Polynomial Representation of Transfer Functions” on page 3-11 in the
“Tutorial” chapter for more information.
For multi-input systems, nb and nk are row vectors, such that the k-th entry
corresponds to the order and delay associated with the k-th input.
If data has no input channels and just one output channel (i.e., it is a time
series), then
4-19
armax
orders = [na nc]
and armax calculates an ARMA model for the time series
A ( q )y ( t ) = C ( q )e ( t )
The structure and the estimation algorithm are affected by any property name/
property value pairs that are set in the input argument list. Useful properties
are 'Focus', 'InitialState', 'Trace', 'MaxIter', 'Tolerance',
'LimitError', and 'FixedParameter'.
See Algorithm Properties, idpoly and idmodel for details of these
properties and their possible values.
armax does not support multi-output models. Use the state-space model for this
case (see n4sid and pem).
Algorithm
A robustified quadratic prediction error criterion is minimized using an
iterative search algorithm, whose details are governed by the properties
'SearchDirection', 'MaxIter','Tolerance' and 'Advanced'. The iterations
are terminated when MaxIter is reached, when the expected improvement is
less than Tolerance, or when a lower value of the criterion cannot be found.
Information about the search is contained in m.EstimationInfo.
The initial parameter values for the iterative search, if not specified in orders,
are constructed in a special four-stage LS-IV algorithm.
The cut-off value for the robustification is based on the property LimitError as
well as on the estimated standard deviation of the residuals from the initial
parameter estimate. It is not recalculated during the minimization.
A stability test of the predictor is performed, so as to assure that only models
corresponding to stable predictors are tested. Generally, both C ( q ) and F i ( q )
(if applicable) must have all their zeros inside the unit circle.
Information about the minimization is furnished to the screen in case the
property 'Trace' is set to 'On' or 'Full'. With 'Trace' = 'Full', current and
previous parameter estimates (in column vector form, listing parameters in
alphabetical order) as well as the values of the criterion function are given. The
Gauss-Newton vector and its norm are also displayed. With 'Trace' = 'On'
just criterion values are displayed.
4-20
armax
See Also
arx, bj, idmodel, idpoly, oe, pem, Algorithm Properties, EstimationInfo
References
Ljung (1999), Section 10.2.
4-21
arx
Purpose
4arx
Estimate the parameters of an ARX or AR model.
Syntax
m = arx(data,orders)
m = arx(data,'na',na,'nb',nb,'nk',nk)
m= arx(data,orders,'Property1',Value1,...,'PropertyN',ValueN)
Description
The parameters of the ARX model structure
A ( q )y ( t ) = B ( q )u ( t – nk ) + e ( t )
are estimated using the least-squares method.
data is an iddata object that contains the output-input data. orders is given
as
orders = [na nb nk]
defining the orders and delay of the ARX model. Specifically,
–1
na:
A ( q ) = 1 + a1 q
nb:
B( q ) = b1 + b2 q
+ … + a na q
–1
– na
+ … + b nb q
– nb + 1
See “Polynomial Representation of Transfer Functions” on page 3-11 in the
“Tutorial” chapter for more information. The model orders can also be defined
by explicit pairs (...,'na',na,'nb',nb,'nk',nk,...).
m is returned as the least-squares estimates of the parameters. For
single-output data this is an idpoly object, otherwise an idarx object.
For a time series, data contains no input channels and orders = na. Then an
AR model of order na for y is computed.
A ( q )y ( t ) = e ( t )
Models with several inputs
A ( q )y ( t ) = B 1 ( q )u 1 ( t – nk 1 ) + …B nu u nu ( t – nk nu ) + e ( t )
are handled by allowing nb and nk to be row vectors defining the orders and
delays associated with each input.
4-22
arx
Models with several inputs and several outputs are handled by allowing na,
nb, and nk to contain one row for each output number. See “Multivariable
ARX Models: The idarx Model” on page 3-37 in the “Tutorial” chapter for exact
definitions.
The algorithm and model structure are affected by the property name/property
value list in the input argument.
Useful options are reached by the properties 'Focus', 'InputDelay', and
'MaxSize'.
See Algorithm Properties for details of these properties and possible values
When the true noise term e ( t ) in the ARX model structure is not white noise
and na is nonzero, the estimate does not give a correct model. It is then better
to use armax, bj, iv4, or oe.
Examples
Here is an example that generates data and estimates an ARX model.
A = [1 -1.5 0.7]; B = [0 1 0.5];
m0 = idpoly(A,B);
u = iddata([],idinput(300,'rbs'));
e = iddata([],randn(300,1));
y = sim(m0, [u e]);
z = [y,u];
m = arx(z,[2 2 1]);
Algorithm
The least-squares estimation problem is an overdetermined set of linear
equations that is solved using QR-factorization.
The regression matrix is formed so that only measured quantities are used (no
fill-out with zeros). When the regression matrix is larger than MaxSize, the
QR-factorization is performed in a for-loop.
See Also
ar, ivx, iv4, Algorithm Properties, EstimationInfo
4-23
arxdata
Purpose
4arxdata
Extract the ARX parameters from idmodel models.
Syntax
[A,B] = arxdata(m)
[A,B,dA,dB] = arxdata(m)
Description
m is the model as an idarx or idpoly model object. arxdata will work on any
idarx model. For idpoly it will give an error unless the underlying model is an
ARX model, i.e., the orders nc=nd=nf=0. (See the reference page for idpoly.)
A and B are returned in the standard multivariable ARX format (see idarx),
describing the model.
y ( t ) + A 1 y ( t – 1 ) + A 2 y ( t – 2 ) + … + A na y ( t – na ) =
B 0 u ( t ) + B 1 u ( t – 1 ) + … + B nb u ( t – nb ) + e ( t )
Here A k and B k are matrices of dimensions ny-by-ny and ny-by-nu, respectively
(ny is the number of outputs, i.e., the dimension of the vector y ( t ) and nu is the
number of inputs). See “Multivariable ARX Models: The idarx Model” on
page 3-37 in the “Tutorial” chapter.
The arguments A and B are 3-D arrays that contain the A matrices and the B
matrices of the model in the following way:
A is an ny-by-ny-by-(na+1) array such that
A(:,:,k+1) = Ak
A(:,:,1) = eye(ny)
Similarly B is an ny-by-nu-by-(nb+1) array with
B(:,:,k+1) = Bk
Note that A always starts with the identity matrix, and that leading entries in
B equal to zero means delays in the model. For a time series B = [].
dA and dB are the estimated standard deviations of A and B.
See Also
4-24
idarx
arxstruc
Purpose
4arxstruc
Compute loss functions for a set of different model structures of single-output
ARX type.
Syntax
V = arxstruc(ze,zv,NN)
V = arxstruc(ze,zv,NN,maxsize)
Description
NN is a matrix that defines a number of different structures of the ARX type.
Each row of NN is of the form
nn = [na nb nk]
with the same interpretation as described for arx. See struc for easy
generation of typical NN matrices for single-input systems.
Each of ze and zv are iddata objects containing output-input data. Models for
each of the model structures defined by NN are estimated using the data set ze.
The loss functions (normalized sum of squared prediction errors) are then
computed for these models when applied to the validation data set zv. The data
sets, ze and zv, need not be of equal size. They could, however, be the same
sets, in which case the computation is faster.
Note that arxstruc is intended for single-output systems only.
The output argument V is best analyzed using selstruc. It contains the loss
functions in its first row. The remaining rows of V contain the transpose of NN,
so that the orders and delays are given just below the corresponding loss
functions. The last column of V contains the number of data points in ze. The
selection of a suitable model structure based on the information in v is
normally done using selstruc. See “Model Structure Selection and Validation”
on page 3-63 in the “Tutorial” chapter for advice on model structure selection
and cross-validation.
See Algorithm Properties for an explanation of maxsize.
4-25
arxstruc
Examples
Compare first to fifth order models with one delay using cross-validation on the
second half of the data set. Then select the order that gives the best fit to the
validation data set.
NN = struc(1:5,1:5,1);
V = arxstruc(z(1:200),z(201:400),NN);
nn = selstruc(V,0);
m = arx(z,nn);
See Also
4-26
arx, ivstruc, n4sid, selstruc, struc
bj
Purpose
4bj
Estimate the parameters of a Box-Jenkins model.
Syntax
m = bj(data,orders)
m = bj(data,'nb',nb,'nc',nc,'nd',nd,'nf',nf,'nk',nk)
m = bj(data,orders,'Property1',Value1,'Property2',Value2,...)
Description
bj returns m as an idpoly object with the resulting parameter estimates,
together with estimated covariances. The bj function estimates parameters of
the Box-Jenkins model structure
B( q)
C(q )
y ( t ) = ------------ u ( t – nk ) + ------------- e ( t )
F( q)
D(q)
using a prediction error method.
data is an iddata object containing the output-input data. The model orders
can be specified by setting the argument orders to
orders = [ nb nc nd nf nk]
The parameters nb, nc, nd, and nf are the orders of the Box-Jenkins model and
nk is the delay. Specifically,
–1
nf:
F ( q ) = 1 + f1q
nb:
B( q ) = b1 + b2 q
nc:
C ( q ) = 1 + c1 q
nd:
D ( q ) = 1 + d1 q
+ … + f nf q
–1
– nf
+ … + b nb q
–1
+ … + c nc q
–1
+ … + d nd q
– nb + 1
– nc
– nd
The orders can also be defined as property name/property value pairs
(...,'nb',nb,...). Alternatively, you can specify the vector as
orders = mi
where mi is an initial guess at the Box-Jenkins model given in idpoly format.
See “Polynomial Representation of Transfer Functions” on page 3-11 in the
“Tutorial” chapter for more information.
4-27
bj
For multi-input systems, nb, nf, and nk are row vectors with as many entries
as there are input channels. Entry number i then describes the orders and
delays associated with the i-th input.
The structure and the estimation algorithm are affected by any property name/
property value pairs that are set in the input argument list. Useful properties
are 'Focus', 'InitialState', 'Trace', 'MaxIter', 'Tolerance',
'LimitError', and 'FixedParameter'.
See Algorithm Properties and the reference pages for idmodel and idpoly for
details of these properties and their possible values.
bj does not support multi-output models. Use state-space model for this case
(see n4sid and pem).
Examples
Here is an example that generates data and stores the results of the startup
procedure separately.
B = [0 1 0.5];
C = [1 -1 0.2];
D = [1 1.5 0.7];
F = [1 -1.5 0.7];
m0 = idpoly(1,B,C,D,F,0.1);
e = iddata([],randn(200,1));
u = iddata([],idinput(200));
y = sim(m0,[u e]);
z = [y u];
mi = bj(z,[2 2 2 2 1],'MaxIter',0)
m = bj(z,mi)
m.EstimationInfo
m = bj(z,m); % Continue if m.es.WhyStop shows that maxiter has
% been reached.
compare(z,m,mi)
Algorithm
bj uses essentially the same algorithm as armax with modifications to the
computation of prediction errors and gradients.
See Also
armax, idmodel, idpoly, oe, pem
4-28
bode
Purpose
4bode
Plot frequency functions in Bode diagram form.
Syntax
bode(m)
[mag,phase,w] = bode(m)
[mag,phase,w,sdmag,sdphase] = bode(m)
bode(m1,m2,m3,...,w)
bode(m1,'PlotStyle1',m2,'PlotStyle2',...)
bode(m1,m2,m3,..'sd',sd,'mode',mode,'ap',ap)
bode(m1,m2,m3,..'sd',sd,'mode',mode,'ap',ap,'fill')
Description
bode computes the magnitude and phase of the frequency response of idmodel
and idfrd models. When invoked without left-hand arguments, bode produces
a Bode plot on the screen.
bode(m) plots the Bode response of an arbitrary idmodel or idfrd model m. This
model can be continuous or discrete, and SISO or MIMO. The InputNames and
OuputNames of the models are used to plot the responses for different I/O
channels in separate plots. Pressing the Enter key advances the plot from one
input-output pair to the next one.
If m contains information about both I/O channels and output noise spectra,
only the I/O channels are shown. To show the output noise spectra enter
m('n') (’n’ for ’noise’) in the model list. Analogously, specific I/O channels can
be selected by the normal subreferencing: m(ky,ku).
Arguments sd, ap, mode and w
The arguments sd, ap, mode and w can appear in any order, or be omitted.
sd: If sd is specified as a number larger than zero, confidence intervals for the
functions are added to the graph as dash-dotted curves (of the same color as the
estimate curve). They indicate the confidence regions corresponding to sd
standard deviations. If an argument 'fill' is included in the argument list,
the confidence region is marked as a filled band instead.
ap: By default, amplitude and phase plots are shown simultaneously for each
I/O channel present in m. For spectra, phase plots are omitted. To show
amplitude plots only, use 'ap'= 'A'. For phase plots only, use 'ap'= 'P'. The
default is 'ap' = 'B' for both plots.
mode: To obtain all plots on the same diagram use mode = 'same'.
4-29
bode
w: bode(m,w) explicitly specifies the frequency range or frequency points to be
used for the plot or for computing the response. To focus on a particular
frequency interval [wmin,wmax], set w = {wmin,wmax} (Notice the curly
brackets). To use particular frequency points, set w to the vector of desired
frequencies. Use logspace to generate logarithmically spaced frequency
vectors. All frequencies should be specified in radians/sec.
Note that the frequencies cannot be specified for idfrd objects. For those the
plot and response are calculated for the internally stored frequencies.
Several Models
bode(m1,m2,...,mN) or bode(m1,m2,...mN,w) plots the Bode response of
several idmodel or idfrd models on a single figure. The models may be mixes
of different sizes and continuous/discrete. The sorting of the plots is made
based on the InputNames and OutputNames. If the frequencies w are specified,
these will apply to all non-idfrd models in the list. If you want different
frequencies for different models, you should thus first convert them to idfrd
objects using the idfrd command.
bode(m1,’PlotStyle1’,...,mN,’PlotStyleN’) further specifies which color,
linestyle and/or marker should be used to plot each system, as in
bode(m1,'r--',m2,'gx')
Arguments
The output argument w contains the frequencies for which the response is
given, whether specified among the input arguments or not. The output
arguments mag and phase are 3-D arrays with dimensions
(number of outputs)x(number of inputs)x(length of w)
For SISO systems mag(1,1,k) and phase(1,1,k) give the magnitude and
phase (in degrees) at the frequency ω k = w(k). To obtain the result as a normal
vector of responses use mag = mag(:) and phase = phase(:).
For MIMO systems mag(i,j,k) is the magnitude of the frequency response at
frequency w(k) from input j to output i, and similairly for phase(i,j,k).
4-30
bode
If sdmag and sdphase are specified, the standard deviations of the magnitude
and phase are also computed. Then sdmag is an array of the same size as mag,
containing the estimated standard deviations of the response, and analogously
for sdphase.
See Also
etfe, ffplot, freqresp, idfrd, nyquist, spa
4-31
compare
Purpose
4compare
Compare measured outputs with model outputs.
Syntax
compare(data,m);
compare(data,m,k,sampnr,init)
compare(data,m1,m2,...,mN,Yplots)
compare(data,m1,'PlotStyle1',...,mN,'PlotStyleN',k,sampnr,init)
[yh,fit] =
compare(data,m1,'PlotStyle1',...,mN,'PlotStyleN',k,sampnr,init)
Description
data is the output-input data in the usual iddata object format.
compare computes the output yh that results when the model m is simulated
with the input u. The result is plotted together with the corresponding
measured output y. The percentage of the ouput variation that is explained by
the model
fit = 100*(1 - norm(yh - y)/norm(y-mean(y)))
is also computed and displayed. For multi-output systems this is done
separately for each output.
When the argument k is specified, the k-step ahead prediction of y according to
the model m are computed instead of the simulated output. In the calculation
of yh ( t ) , the model can use outputs up to time t – k : y ( s ), s = t – k , t – k – 1 ,
… (and inputs up to the current time t). The default value of k is inf, which
gives a pure simulation from the input only.
A last argument Yplots may be given as a cell array of strings. Only the
outputs with OutputName in this array will be plotted, while all are used for the
necessary computations. If Yplots is not specified, all outputs will be plotted.
The argument sampnr indicates that only the sample numbers in this row
vector are plotted and used for the calculation of the fit. The whole data record
is used for the simulation/prediction, though.
The argument init determines how to handle initial conditions in the models:
init = 'e' (for 'estimate') estimates the initial conditions for best fit.
init = 'm' (for 'model') used the model’s internally stored initial state.
init = 'z' (for 'zero') uses zero initial conditions.
4-32
compare
init = x0, where x0 is a column vector of the same size as the state vector of
the models, uses x0 as the initial state.
init = 'e' is default.
When several models are specified, as in compare(data,m1,m2,...,mN), the
plots show responses and fits for all models. In that case data should contain
all inputs and outputs that are required for the different models. However,
some models may very well correspond to subselections of channels and may
not need all channels in data. In that case the proper handling of signals is
based on the InputNames and OutputNames of data and the models.
With compare(data,m1,'PlotStyle1',...mN,'PlotStyle2') the color,
linestyle, and/or marker can be specified for the curves associated with the
different models. The markers are the same as for the regular plot command.
For example,
compare(data,m1,'g_*',m2,'r:')
If data contains several experiments, separate plots are given for the different
experiments. In this case sampnr, if specified, must be a cell array with as many
entries as there are experiments.
Arguments
When output arguments [yh,fit] = compare(data,m1,..,mN) are specified,
no plots are produced.
yh is a cell array of length equal to the number of models. Each cell contains
the corresponding model output as an iddata object.
fit is in the general case a 3-D array with fit(kexp,kmod,ky) containing the
fit (computed as above) for output ky, model kmod, and experiment kexp.
Examples
Split the data record into two parts. Use the first one for estimating a model
and the second one to check the model’s ability to predict six steps ahead:
ze = z(1:250);
zv = z(251:500);
m= armax(ze,[2 3 1 0]);
compare(zv,m,6);
See Also
sim, predict
4-33
covf
Purpose
4covf
Estimate time-series covariance functions.
Syntax
R = covf(data,M)
R = covf(data,M,maxsize)
Description
data is an iddata object and M is the maximum delay -1 for which the
covariance function is estimated.
Let z contain the output and input channels
z(t) =
y(t)
u(t)
where y and u are the rows of data.OutputData and data.InputData,
respectively, with a total of nz channels.
R is returned as an nz2 -by- M matrix with entries
N
1
R ( i + ( j – 1 )nz ,k + 1 ) = ---N
∑ zi ( t )zj ( t + k )
ˆ (k)
= R
ij
t=1
where z j is the j-th row of z and missing values in the sum are replaced by
zero.
The optional argument maxsize controls the memory size as explained under
Algorithm Properties.
The easiest way to describe and unpack the result is to use
reshape(R(:,k+1),nz,nz) = E z(t)∗z'(t+k)
Here ' is complex conjugate transpose, which also explains how complex data
are handled. The expectation symbol E corresponds to the sample means.
Algorithm
When nz is at most two, and when permitted by maxsize, a fast Fourier
transform technique is applied. Otherwise, straightforward summing is used.
See Also
spa
4-34
cra
Purpose
4cra
Perform prewhitening based correlation analysis and estimate impulse
response.
Syntax
cra(data);
[ir,R,cl] = cra(data,M,na,plot);
cra(R);
Description
data is the output-input data given as an iddata object.
The routine only handles single-input-single-output data pairs. (For the
multivariate case, apply cra to two signals at a time, or use impulse.) cra
prewhitens the input sequence, i.e., filters u through a filter chosen so that the
result is as uncorrelated (white) as possible. The output y is subjected to the
same filter, and then the covariance functions of the filtered y and u are
computed and graphed. The cross correlation function between (prewhitened)
input and output is also computed and graphed. Positive values of the lag
variable then corresponds to an influence from u to later values of y. In other
words, significant correlation for negative lags is an indication of feedback from
y to u in the data.
A properly scaled version of this correlation function is also an estimate of the
system’s impulse response ir. This is also graphed along with 99% confidence
levels. The output argument ir is this impulse response estimate, so that its
first entry corresponds to lag zero. (Negative lags are excluded in ir.) In the
plot, the impulse response is scaled, so that it corresponds to an impulse of
height 1/T and duration T, where T is the sampling interval of the data.
The output argument R contains the covariance/correlation information as
follows: The first column of R contains the lag indices. The second column
contains the covariance function of the (possibly filtered) output. The third
column contains the covariance function of the (possibly prewhitened) input,
and the fourth column contains the correlation function. The plots can be
redisplayed by cra(R).
The output argument cl is the 99% confidence level for the impulse response
estimate.
The optional argument M defines the number of lags for which the covariance/
correlation functions are computed. These are from –M to M, so that the length
4-35
cra
of R is 2M+1. The impulse response is computed from 0 to M. The default value
of M is 20.
For the prewhitening, the input is fitted to an AR model of order na. The third
argument of cra can change this order from its default value na = 10. With na
= 0 the covariance and correlation functions of the original data sequences are
obtained.
plot: plot = 0 gives no plots. plot = 1 (default) gives a plot of the estimated
impulse response together with a 99% confidence region. plot = 2 gives a plot
of all the covariance functions.
An often better alternative to cra are the functions impulse and step, which
use a high order FIR model to estimate the impulse response.
Examples
Compare a second order ARX model’s impulse response with the one obtained
by correlation analysis.
ir = cra(z);
m = arx(z,[2 2 1]);
imp = [1;zeros(19,1)];
irth = sim(m,imp);
subplot(211)
plot([ir irth])
title('impulse responses')
subplot(212)
plot([cumsum(ir),cumsum(irth)])
title('step responses')
See Also
4-36
impulse, step
c2d
Purpose
4c2d
Convert a model from continuous time to discrete time.
Syntax
md = c2d(mc,T)
md = c2d(mc,T,method)
Description
mc is a continuous-time model as any idmodel object (idgrey, idpoly, or idss).
md is the model that is obtained when it is sampled with sampling interval T.
Note that the covariance matrix of mc is not translated.
method = 'zoh' (default) makes the translation to discrete time under the
assumption that the input is piecewise constant (zero-order hold).
method = 'foh' assumed the input to be piecewise linear between the
sampling instants (first-order-hold).
Note that the innovations variance λ of the continuous-time model is
interpreted as the intensity of the spectral density of the noise spectrum. The
noise variance in md will thus be given as λ /T.
idpoly and idss models are returned in the same format. idgrey structures
will be preserved if their CDMfile property is equal to ‘cd’. Otherwise they will
be transformed to idss objects.
Examples
Define a continuous-time system and study the poles and zeros of the sampled
counterpart.
mc = idpoly(1,1,1,1,[1 1 0],'Ts',0);
md = c2d(mc,0.5);
pzmap(md)
See Also
d2c
4-37
detrend
Purpose
4detrend
Remove trends from output-input data.
Syntax
zd = detrend(z)
zd = detrend(z,o,brkp)
Description
z is an iddata object containing the input-output data. detrend removes the
trend from each signal and returns the result as an iddata object zd.
The default (o = 0) removes the zero-th order trends, i.e., the sample means
are subtracted.
With o = 1, linear trends are removed, after a least-squares fit. With brkp not
specified, one single line is subtracted from the entire data record. A
continuous piecewise linear trend is subtracted if brkp contains breakpoints at
sample numbers given in a row vector.
Note that detrend for iddata objects differs somewhat from detrend in the
Signal Processing Toolbox.
Examples
Remove a V-shaped trend from the output with its peak at sample number 119,
and remove the sample mean from the input.
zd(:,1,[]) = detrend(z(:,1,[]),1,119);
zd(:,[],1) = detrend(z(:,[],1));
4-38
d2c
Purpose
4d2c
Convert a model from discrete to continuous time.
Syntax
mc = d2c(md)
mc = d2c(md,'CovarianceMatrix',cov,'InputDelay',inpd)
Description
The discrete-time model md, given as any idmodel object, is converted to a
continuous-time counterpart mc. The covariance matrix of the parameters in
the model is also translated using Gauss’ approximation formula and
numerical derivatives of the transformation. The step-sizes in the numerical
derivatives are determined by the function nuderst. To inhibit the translation
of the covariance matrix and save time, enter among the input arguments
(...,'CovarianceMatrix,'None',....) (any abbreviations will do).
If the discrete-time model contains pure time delays, i.e., nk > 1 , then these
are first removed before the transformation is made. These delays are
appended as pure time-delay (deadtime) to the continuous-time model as the
property InputDelay. To have the time delay approximated by a
finite-dimensional continuous system, enter among the input arguments
(...,'InputDelay',0,...).
If the noise variance is λ in md, and its sampling interval is T, then the
continuous-time model has an indicated level of noise spectral density equal to
T λ.
While idpoly and idss models are returned in the same format, idarx models
are returned as idss models mc. The reason is that the transformation does not
preserve the special structure of idarx. idgrey structures will be preserved if
their CDMfile property is equal to cd. Otherwise they will be transformed to
idss objects.
Note The transformation from discrete to continuous time is not unique. d2c
selects the continuous-time counterpart with the slowest time constants,
consistent with the discrete-time model. The lack of uniqueness also means
that the transformation may be ill-conditioned or even singular. In particular,
poles on the negative real axis, in the origin, or in the point 1, are likely to
cause problems. Interpret the results with care.
4-39
d2c
Examples
Transform an identified model to continuous time and compare the frequency
responses of the two models.
m = n4sid(data,3)
mc = d2c(m);
bode(m.mc,'sd',3)
Note that the transformation to continuous time can be included in the n4sid
command by specifying the model to be continuos time.
mc = n4sid(data,3,'Ts',0)
See Also
c2d, nuderst
References
See “Discrete and Continuous Time Models” on page 3-60 and “Spectrum
Normalization and the Sampling Interval” on page 3-94” in the “Tutorial”
chapter.
4-40
EstimationInfo
Purpose
4EstimationInfo
To provide information about the results of the estimation process
Syntax
m.EstimationInfo
m.es
m.es.DataLength, etc
Description
Any estimated model has the property EstimationInfo, which is a structure
whose fields give information about the results of the estimation. The model
structure will contain the properties ParameterVector, CovarianceMatrix,
and NoiseVariance, which are all calculated in the estimation process (see the
reference page for idmodel). In addition, EstimationInfo contains the
following fields:
• Status: Information whether the model has been estimated, or modified
after being estimated.
• Method: The name of the estimation command that produced the model.
• LossFcn: The value of the identification criterion at the estimate. Normally
equal to the determinant of the covariance matrix of the prediction errors,
i.e., the determinant of NoiseVariance. Note that the loss function for the
minimization might be different due to LimitError. In LossFcn, always the
value of the non-robustified loss function is stored.
• FPE: Akaikes Final Prediction Error, defined as LossFcn *(1+d/N}/(1-d/N)
where d is the number of estimated parameters and N is the length of the
data record.
• DataName: Name of the data set from which the model was estimated. This is
equal to the property name of the iddata object. If this was not defined, the
name of the MATLAB iddata variable is used.
• DataLength: The length of the data record
• DataTs: The sampling interval of the data
• DataInterSample: The intersample behavior of the data from which the
model was estimated. This equals the property InterSample of the iddata
object. (See iddata.)
• WhyStop: For models that have been estimated by iterative search. The
stopping rule that caused the iterations to terminate. Assumes values like
'MaxIter reached','No improvement possible along the search
4-41
EstimationInfo
vector' or 'Near (local) minimum'. The latter means that the expected
improvement is less than Tolerance (see Algorithm Properties).
• UpdateNorm: The norm of the Gauss-Newton vector at the last iteration
• LastImprovement: The relative improvement of the criterion value at the
last iteration.
• Iterations: The number of iterations used in the search.
• InitialState: The actually used option when Model.InitialState =
'auto'.
• N4Weight: For n4sid estimates, or estimates that have been initialized by
n4sid: the actual value of N4Weight used.
• N4Horizon: For n4sid estimates, or estimates that have been initialized by
n4sid: the actual value of N4Horizon used. See n4sid and Algorithm
Properties.
See Also
4-42
idfrd
etfe
Purpose
4etfe
Estimate empirical transfer functions and periodograms.
Syntax
g = etfe(data)
g = etfe(data,M,N)
Description
etfe estimates the transfer function g of the general linear model
y ( t ) = G ( q )u ( t ) + v ( t )
data contains the output-input data and is an iddata object.
g is given as an idfrd object with the estimate of G ( e
iω
) at the frequencies
w = [1:N]/N∗pi/T
The default value of N is 128.
In case data contains a time series (no input channels), g is returned as the
periodogram of y.
When M is specified other than the default value M = [], a smoothing operation
is performed on the raw spectral estimates. The effect of M is then similar to the
effect of M in spa. This can be a useful alternative to spa for narrowband spectra
and systems, which require large values of M.
When etfe is applied to time series, the corresponding spectral estimate is
normalized in the way that is defined in the section “Spectrum Normalization
and the Sampling Interval” on page 3-94 in the Tutorial. Note that this
normalization may differ from the one used by spectrum in the Signal
Processing Toolbox.
If the (input) data is marked as periodic (data.Period = integer) and contains
an even number of periods, the response is computed at the frequencies
k*2*pi/period for k=0 up to the Nyquist frequency.
Examples
Compare an empirical transfer function estimate to a smoothed spectral
estimate.
ge = etfe(z);
gs = spa(z);
bode(ge,gs)
4-43
etfe
Generate a periodic input, simulate a system with it, and compare the
frequency response of the estimated model with the true system at the excited
frequency points.
m = idpoly([1 -1.5 0.7],[0 1 0.5]);
u = iddata([],idinput([50,1,10],'sine'));
u.Period = 50;
y = sim(m,u);
me = etfe([y u])
bode(me,'b*',m)
Algorithm
The empirical transfer function estimate is computed as the ratio of the output
Fourier transform to the input Fourier transform, using fft. The periodogram
is computed as the normalized absolute square of the Fourier transform of the
time series.
The smoothed versions (M less than the length of z) are obtained by applying a
Hamming window to the output fast Fourier transform (FFT) times the
conjugate of the input FFT, and to the absolute square of the input FFT,
respectively, and subsequently forming the ratio of the results. The length of
this Hamming window is equal to the number of data points in z divided by M,
plus one.
See Also
4-44
spa
ffplot
Purpose
4ffplot
Plot frequency functions and spectra.
Syntax
ffplot(m)
[mag,phase,w] = ffplot(m)
[mag,phase,w,sdmag,sdphase] = ffplot(m)
ffplot(m1,m2,m3,...,w)
ffplot(m1,'PlotStyle1',m2,'PlotStyle2',...)
ffplot(m1,m2,m3,..'sd',sd,'mode',mode,'ap',ap)
Description
This function has exactly the same syntax as bode. The only difference is that
it gives graphs with linear frequency scales and Hz as the frequency unit.
See Also
bode, nyquist
4-45
freqresp
Purpose
4freqresp
Compute the frequency function for a model.
Syntax
H = freqresp(m)
[H,w,covH] = freqresp(m,w)
Description
m is any idmodel or idfrd object.
H = freqresp(m,w) computes the frequency response H of the idmodel model
m at the frequencies specified by the vector w. These frequencies should be real
and in radians/second.
If m has ny outputs and nu inputs, and w contains Nw frequencies, the output
H is a ny-by-nu-by-Nw array such that H(:,:,k) gives the complex valued
response at the frequency w(k).
For a SISO model, H(:) to obtain a vector of the frequency response.
If w is not specified, a default choice is made. For a discrete-time model w will
be 128 equally spaced frequency points from 0 (excluded) to the Nyquist
frequency. For a continuous-time model, the default is
w=logspace(log10(pi/abs(Ts)/100),log10(10*pi/abs(Ts)),128)'
where Ts is the sampling interval of the data from which the model was
estimated. If the model is not estimated, Ts is taken as 1, which may make it
necessary to specify w as in input argument in this case.
[H,w,covH] = freqresp(M,w)
also returns the frequencies w and the covariance covH of the response. covH is
a 5-D array where covH(ky,ku,k,:,:) is the 2-by-2 covariance matrix of the
response from input ku to output ky at frequency w(k). The 1,1 element is the
variance of the real part, the 2,2 element the variance of the imaginary part
and the 1,2 and 2,1 elements the covariance between the real and imaginary
parts. squeeze(covH(ky,ku,k,:,:)) gives the covariance matrix of the
corresponding response.
If m is a time series (no input channels), H is returned as the (power) spectrum
of the outputs; an ny-by-ny-by-Nw array. Hence H(:,:,k) is the spectrum
matrix at frequency w(k). The element H(k1,k2,k) is the cross spectrum
between outputs k1 and k2 at frequency w(k). When k1=k2, this is the
real-valued power spectrum of output k1.
4-46
freqresp
covH is then the covariance of the estimated spectrum H, so that
covH(k1,k1,k) is the variance of the power spectrum estimate of output k1 at
frequency W(k). No information about the variance of the cross spectra is
normally given i.e., covH(k1,k2,k) = 0 for k1 not equal to k2.)
If the model m is not a time series, use freqresp(m('n')) to obtain the
spectrum information of the noise (output disturbance) signals.
Note that idfrd computes the same information as freqresp, and stores it in
the idfrd object.
See Also
bode, idfrd, nyquist
4-47
fpe
Purpose
4fpe
Compute the Akaike Final Prediction Error for an estimated model
Syntax
am = fpe(Model)
Description
Model is any estimated idmodel (idarx, idgrey, idpoly, idss).
am is returned as the value of the Akaike Final Prediction Error
1+d⁄N
FPE = V ---------------------1–d⁄N
where V is the loss function, d is the number of estimated parameters, and N
is the number of estimation data.
See Also
EstimationInfo, aic
Reference
Sections 7.4 and 16.4 in Ljung (1999)
4-48
get
Purpose
4get
Access/query idmodel, idfrd, and iddata properties.
Syntax
Value = get(m,'PropertyName')
get(m)
Struct = get(m)
Description
value = get(m,'PropertyName') returns the current value of the property
PropertyName of the iddata set or idfrd, or idmodel (idgrey, idarx, idpoly,
idss) m. The string 'PropertyName' can be the full property name (e.g.,
'SSParameterization') an any unambiguous case-insensitive abbreviation
thereof (e.g., 'ss'). You can specify any generic idmodel property or any
property specific to idgrey, idarx, etc. (see iddata, idmodel, idgrey, idarx,
idpoly, idss, and Algorithm Properties for lists of properties that can be
accessed directly).
Struct = get(m) converts the object m into a standard MATLAB structure
with the property names as field names and the property values as field values.
Without left-hand argument
get(m)
displays all properties of m and their values.
Remark
An alternative to the syntax
Value = get(m,'PropertyName')
is the structure-like referencing
Value = m.PropertyName
See Also
arxdata, iddata, idfrd, idmodel, polydata, set, ssdata, tfdata, zpkdata,
Algorithm Properties, EstimationInfo
4-49
getexp
4getexp
Purpose
Syntax
Description
Retrieve separate experiment(s) from multi-experiment iddata objects.
d1 = getexp(data,ExperimentNumber)
d1 = getexp(data,ExperimentName)
data is an iddata object that contains several experiments. d1 will be another
iddata object containing the indicated experiment(s). The reference can either
be by ExperimentNumber as in d1 = getexp(data,3) or d1 = getexp(data,[4
2]), or by ExperimentName as in d1 = getexp(data,'Period1') or
d1 = getexp(data,'Day1','Day3').
See merge (iddata) and iddata for how to create multi-experiment data
objects.
The experiments can also be retrieved by a fourth subscript as in
d1 = data(:,:,:,ExperimentNumber). See help iddata/subsref for details
on this.
4-50
idarx
4idarx
Purpose
Construct idarx model from ARX polynomials.
Syntax
m = idarx(A,B,Ts)
m = idarx(A,B,Ts,'Property1',Value1,...,,'PropertyN',ValueN)
Description
idarx creates an object containing parameters that describe the general
multi-input, multi-output model structure of ARX type.
y ( t ) + A 1 y ( t – 1 ) + A 2 y ( t – 2 ) + … + A na y ( t – na ) =
B 0 u ( t ) + B 1 u ( t – 1 ) + … + B nb u ( t – nb ) + e ( t )
Here A k and B k are matrices of dimensions ny-by-ny and ny-by-nu, respectively
(ny is the number of outputs, i.e., the dimension of the vector y ( t ) and nu is the
number of inputs). See “Multivariable ARX Models: The idarx Model” on
page 3-37 in the “Tutorial” chapter.
The arguments A and B are 3-D arrays that contain the A matrices and the B
matrices of the model in the following way.
A is an ny-by-ny-by-(na+1) array such that
A(:,:,k+1) = Ak
A(:,:,1) = eye(ny)
Similairly B is an ny-by-nu-by-(nb+1) array with
B(:,:,k+1) = Bk
Note that A always starts with the identity matrix, and that delays in the model
are defined by setting the corresponding leading entries in B to zero. For a
multivariate time series take B = [].
The optional property NoiseVariance sets the covariance matrix of the driving
noise source e ( t ) in the model above. The default value is the identity matrix.
4-51
idarx
The argument Ts is the sampling interval.
The use of idarx is twofold. You can use it to create models that are simulated
(using sim) or analyzed (using bode, pzmap, etc.). You can also use it to define
initial value models that are further adjusted to data (using arx). The free
parameters in the structure are consistent with the structure of A and B, i.e.,
leading zeros in the rows of B are regarded as fixed delays, and trailing zeros in
A and B are regarded as a definition of lower order polynomials. These zeros are
fixed, while all other parameters are free.
For a model with one output, ARX models can be descried both as idarx and
idpoly models. The internal representation is however different.
idarx
Properties
• A, B: The A and B polynomials as 3-D arrays, described above
• dA, dB: The standard deviations of A and B. Same format as A and B. Cannot
be set.
• na, nb, nk: The orders and delays of the model. na is a ny-by-ny matrix
whose i-j entry is the order of the polynomial corresponding to the i-j entry
of A. Similarly nb is an ny-by-nu matrix with the orders of the B. nk is also an
ny-by-nu matrix, whose i-j entry is the delay from input j to output i, that is,
the number of leading zeros in the i-j entry of B.
In addition to these properties, idarx objects also have all the properties of the
idmodel object. See idmodel, Algorithm Properties, and EstimationInfo.
Note that all properties can be set and retrieved either by set/get or by
subscripts. Autofill applies to all properties and values, and these are case
insensitive.
For a complete list of property values, use get(m). To see possible value
assignments, use set(m). See also idprops idarx.
4-52
idarx
Examples
Simulate a second order ARX model with one input and two outputs, and then
estimate a model using the simulated data.
A = zeros(2,2,3);
B = zeros(2,1,3)
A(:,:,1) =eye(2);
A(:,:,2) = [-1.5 0.1;-0.2 1.5];
A(:,:,3) = [0.7 -0.3;0.1 0.7];
B(:,:,2) = [1;-1];
B(:,:,3) = [0.5;1.2];
m0 = idarx(A,B,1);
u = iddata([],idinput(300));
e = iddata([],randn(300,2));
y = sim(m0,[u e]);
m = arx([y u],[[2 2;2 2],[2;2],[1;1]]);
See Also
arx, arxdata, idmodel, idpoly
4-53
iddata
Purpose
4iddata
Package input-output data into the iddata object.
Syntax
data = iddata(y,u)
data = iddata(y,u,Ts,'Property1',Value1,...,'PropertyN',ValueN)
Description
iddata is the basic object for dealing with signals in the toolbox. It is used by
most of the commands.
Basic Use
Let y be a column vector or an N-by-ny matrix. The columns of y correspond to
the different output channels. Similarly, u is a column vector or an N-by-nu
matrix containing the signals of the input channels.
data = iddata(y,u,Ts)
creates an iddata object containing these output and input channels. Ts is the
sampling interval. This construction is sufficient for most purposes.
The data is then plotted by plot(data) (see plot), and portions of the data
record are selected as in ze = data(1:300) or zv = data(501:700).
The signals in the output channels are retrieved by data.OutputData, or for
short data.y. Similarly the input signals are obtained by data.InputData or
data.u.
For a time series (no input channels) use data = iddata(y), or let u = [].
An iddata object can also contain just an input, by letting y = [].
The sampling interval can be changed by set(data,'Ts',0.3) or, simpler, by
data.Ts = 0.3.
The input and output channels are given default names like 'y1', 'y2',
'u1','u2', etc. The channel names can be set by
set(data,'InputName',{'Voltage','Current'},'OutputName','Tempera
ture')
(two inputs and one output is this example) and these names will then follow
the object and appear in all plots. The names will also be inherited by models
that are estimated from the data.
4-54
iddata
Similarly, channel units can be specified using the properties 'OutputUnit'
and 'InputUnit'. These units, when specified, will be used in plots.
The time points associated with the data samples are determined by the
sampling interval Ts and the time of the first sample, Tstart.
data.Tstart = 24
The actual time point values are given by the property 'SamplingInstants',
as in
plot(data.sa,data.u)
for a plot of the input with correct time points. Autofill is used for all properties,
and they are case insensitive.
Manipulating Channels
An easy way to set and retrieve channel properties is to use subscripting. The
subscripts are defined as
data(Samples,Outputs,Inputs)
so dat(:,3,:) is the data object obtained from dat by keeping all input
channels, but only output channel 3. (Trailing “:”s can be omitted so
dat(:,3,:)= dat(:,3).).
The channels can also be retrieved by their names, so that
dat(:,{'speed','flow'},[])
is the data object where the indicated output channels have been selected and
no input channels are selected.
Moreover
dat1(101:200,[3 4],[1 3]) = dat2(1001:1100,[1 2],[6 7])
will change samples 101 to 200 of output channels 3 and 4 and input channels
1 and 3 in the iddata object dat1 to the indicated values from iddata object
dat2. The names and units of these channels will then also be changed
accordingly.
To add new channels, use horizontal concatenation of iddata objects.
dat =[dat1, dat2];
4-55
iddata
(see “Horizontal Concatenation” below) or add the data record directly. Thus
dat.u(:,5) = U
will add a fifth input to dat.
Nonequal Sampling
The property 'SamplingInstants' gives the sampling instants of the data
points. It can always be retrieved by get(DAT,'SamplingInstants') (or dat.s)
and is then computed from dat.Ts and dat.Tstart. 'SamplingInstants' can
also be set to an arbitrary vector of the same length as the data, so that
nonequal sampling can be handled. Ts is then automatically set to [ ]. Most of
the estimation routines, though, do not handle unequally sampled data.
Multiple Experiments
The iddata object can also store data from separate experiments. The property
'ExperimentName' is used to separate the experiments. The number of data as
well as the sampling properties can vary from experiment to experiment, but
the input and output channels must be the same. (Use NaN to fill possibly
unmeasured channels in certain experiments.) The data records will be cell
arrays, where the cells contain data from each experiment.
Multiple experiments can be defined directly by letting the 'y' and 'u'
properties as well as 'Ts' and 'Tstart' be cell arrays.
It is normally easier to create multi-experiment data by merging experiments
as in
dat = merge(dat1,dat2)
See the reference page for merge (data). Storing multiple experiments as one
iddata object may be very useful to handle experimental data that has been
collected on different occasions, or when a data set has been split up to remove
“bad” portions of the data. All the toolbox’s routines accept multiple
experiment data.
Experiments can be retrieved by the command getexp. They can also be
retrieved by subscripting with a fourth index: dat(:,:,:,3) is experiment
number 3 and dat(:,:,:,{'Day1','Day4'}) retrieves the two experiments
with the indicated names.
4-56
iddata
The subscripting can be combined: dat(1:100,[2,3],[4:8],3) gives the 100
first samples of output channels 2 and 3 and input channels 4 to 8 of
experiment number 3. It can also be used for subassignment, like
dat(:,:,:,'Run4') = dat2
which adds the data in dat2 as a new experiment with name 'Run4'. See
iddemo number 8 for an illustration of how multiple experiments can be used.
iddata
Properties
In the list below, N denotes the number of samples of the signals, ny the number
of output channels, nu the number of input channels, and Ne the number of
experiments:
• Domain: Assumes the values 'Time' or 'Frequency' and denotes whether the
data are time domain or frequency domain data.
• Name: An optional name for the data set. An arbitrary string.
• OutputData, InputData: The data matrices y and u. In the single
experiment case y is an N-by-ny matrix and u is an N-by-nu matrix. For
multiple experiments y and u are 1-by-Ne cell arrays, with each cell
containing the data for the different experiments.
• OutputName, InputName: Cell arrays of length ny-by-1 and nu-by-1
containing the names of the output and input channels. If not specified,
default names, {'y1';'y2';...} and {'u1';'u2';...} are given.
• OutputUnit, InputUnit: Cell arrays of length ny-by-1 and nu-by-1
containing the units of the output and input channels.
• TimeUnit: The unit for the sampling instants.
• Ts: Sampling interval. A positive scalar. For multiexperiment data, Ts is a
1-by-Ne cell array, with each cell containing the sampling interval of the
corresponding experiment. For nonequally sampled data, Ts = [].
• Tstart: The starting time of the data record. A scalar. For multiexperiment
data, Tstart is a 1-by-Ne cell array, with each cell containing the starting
time for the corresponding experiment
• SamplingInstants: The time values of the sample points. A N-by-1 vector.
For multiple experiment data, SamplingInstants is a 1-by-Ne cell array,
with each cell containing the sampling instants of the corresponding
experiment. For equally sampled data, SamplingInstants is generated from
Ts and Tstart.
4-57
iddata
• Period: The period of the input. A nu-by-1 vector, where the k-th entry
contains the period of the k-th input. Period = inf means nonperiodic data.
For multiexperiment data, Period is a 1-by-Ne cell array with each cell
containing the period(s) for the input of the corresponding experiment.
• InterSample: Describes the intersample behavior of the input channels. An
nu-by-1 cell array where the (k,1) element is 'zoh', 'foh', or 'bl',
denoting that input number k is piecewise constant, piecewise linear, or
band limited. For multiple experiment data, InterSample is an nu-by-Ne cell
array.
• ExperimentName: A string containing the name of the experiment. For
multiple experiment data ExperimentName is a 1-by-Ne cell array with each
cell containing the name of the corresponding experiment. It can be freely
set, and is by default given names {'Exp1', 'Exp2',...}.
• Notes: An arbitrary field to store extra information and notes about the
object.
• UserData: An arbitrary field for any possible use.
Note that all properties can be set or retrieved either by set/get or by
subscripts. Autofill applies to all properties and values, and are case
insensitive:. 'y' and 'u' can be used as short for 'OutputData' and
'InputData'. 'y' and 'u' can also replace 'Output' and 'Input' in the other
properties.
data.y=randn(100,2)
data.una = 'Voltage'
set(data,'tim','minute')
p = data.per
For a complete list of property values, use get(data). To see possible value
assignments, use set(data).
Subreferencing
The samples, outputs and input channels can be referenced according to
data(samples,outputs,inputs)
Use a colon (:) to denote all samples/channels and the empty matrix ([ ]) to
denote no samples/channels. The channels can be referenced by number or by
name. For several names, a cell array must be used.
4-58
iddata
dat2 = dat(:,'y3',{'u1','u4'})
dat2 = dat(:,3,[1 4])
Logical expressions will also work.
dat3 = dat2(dat2.sa>1.27&dat2.sa<9.3)
will select the samples with time marks between 1.27 and 9.3.
Subreferencing with curly brackets refers to the experiment.
data{Experiment}(samples,outputs,inputs)
Any subreferenced variable can also be assigned.
data{'Exp3'}.y = flow(1:700,:)
data(1:10,1,1) = dat1(101:110,2,3)
Horizontal
Concatenation
dat = [dat1,dat2,...,datN]
creates an iddata object dat, consisting of the input and output channels in
dat1,... datN. Default channel names ('u1', 'u2', 'y1', 'y2', etc.) will
be changed so that overlaps in names are avoided, and the new channels will
be added.
If datk contains channels with user specified names, that are already present
in the channels of Datj, j<k, these new channels will be ignored.
Vertical
Concatenation
dat = [dat1;dat2;... ;datN]
creates an iddata object dat whose signals are obtained by stacking those of
datk on top of each other. That is
dat.OutputData = [dat1.Ouputdata;dat2.OutputData; ...
datN.OutputData]
and similarly for the inputs. The datk objects must all have the same number
of channels and experiments.
Online Help
Functions
See help iddata, idprops iddata, help iddata/subsref, help iddata/
subsasgn, help iddata/horzcat, and help iddata/vertcat.
See Also
plot (iddata), size
4-59
ident
Purpose
4ident
Open the graphical user interface.
Syntax
ident
ident(session,directory)
Description
ident by itself opens the main interface window, or brings it forward if it is
already open.
session is the name of a previous session with the graphical interface, and
typically has extension .sid. directory is the complete path for the location of
this file. If the session file is on the MATLABPATH, directory can be omitted.
When the session is specified, the interface will open with this session active.
Typing ident(session,directory) on the MATLAB command line, when the
interface is active, will load and open the session in question.
For more information about the graphical user interface, see Chapter 2, “The
Graphical User Interface.”
Examples
4-60
ident('iddata1.sid')
ident('mydata.sid','\matlab\data\cdplayer\')
idfilt
Purpose
4idfilt
Filter data using general filters or Butterworth filters.
Syntax
zf = idfilt(z,filter)
zf = idfilt(z,ord,Wn)
zf = idfilt(z,ord,causality)
[zf,mf] = idfilt(z,ord,Wn,hs)
Description
z is the data, defined as an iddata object. zf contains the filtered data as an
iddata object. The filter can be defined in two ways:
• As an explicit argument filter. This in turn can be given either as any SISO
idmodel or LTI model object, or as a cell array {A,B,C,D} of SISO state-space
matrices or as a cell array {num,den} of numerator/denominator filter
coefficients.
• As a triplet of arguments ...,ord,Wn,hs,..., which defines a Butterworth
filter of order ord. If hs is not specified and Wn contains just one element, a
low pass filter with cutoff frequency Wn (measured as a fraction of the
Nyquist frequency) is obtained. If hs =' high' a high pass filter with this
cutoff frequency is obtained instead. If Wn = [Wnl Wnh] is a vector with two
elements, a filter (of order 2*ord) with passband between Wnl and Wnh is
obtained is hs is not specified. If hs = 'stop' a bandstop filter with stop band
between these two frequencies is obtained instead.
The output argument mf is the filter given as an idmodel object.
With causality = 'causal' (default) causal filtering is used. With
causality = 'noncausal', a noncausal, zero-phase filter is used for the
filtering.
It is common practice in identification to select a frequency band where the fit
between model and data is concentrated. Often this corresponds to bandpass
filtering with a pass band over the interesting breakpoints in a Bode diagram.
For identification where a disturbance model also is estimated, it is better to
achieve the desired estimation result by using the property 'Focus' (see
Algorithm Properties) than just to prefilter the data.
4-61
idfilt
Algorithm
The Butterworth filter is the same as butter in the Signal Processing Toolbox.
Also, the zero-phase filter is equivalent to filtfilt in that toolbox.
References
Ljung (1999), Chapter 14.
4-62
idfrd
Purpose
Create the idfrd (Identified Frequency Response Data) object that stores
frequency function and spectrum data along with covariance information.
Syntax
h = idfrd(Response,Freqs,Ts)
h = idfrd(Response,Freqs,Ts,'CovarianceData',Covariance, ...
'SpectrumData',Spec,'NoiseCovariance',Speccov,'property1', ...
Value1,'PropertyN',ValueN)
h = idfrd(mod)
h = idfrd(mod,Freqs)
Description
idfrd creates the idfrd model object.
4idfrd
For a model
y ( t ) = G ( q )u ( t ) + H ( q )e ( t )
stores the transfer function estimate G (see equation (Equation 3-4) in the
“Tutorial” chapter)
iω
G(e )
as well as the spectrum of the additive noise ( Φ v ) at the outputs
Φ v ( ω ) = λT H ( e
iωT 2
)
where λ is the estimated variance of e(t), and T is the sampling interval.
Creating idfrd from Given Responses
Response is a 3-D array of dimension ny-by-nu-by-Nf with ny being the number
of outputs, nu the number of inputs, and Nf the number of frequencies (i.e., the
length of Freqs). Response(ky,ku,kf) is thus the complex-valued frequency
response from input ku to output ky at frequency ω =Freqs(kf). When defining
the response of a SISO system, Response can be given as a vector.
Freqs is a column vector of length Nf containing the frequencies of the
response.
Ts is the sampling interval. T = 0 means a continuous time model.
Covariance is a 5-D array containing the covariance of the frequency response.
It has dimension ny-by-nu-by-Nf-by-2-by-2. The structure is such that
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idfrd
Covariance(ky,ku,kf,:,:) is the 2-by-2 covariance matrix of the response
Response(ky,ku,kf). The 1-1 element is the variance of the real part, the 2-2
element is the variance of the imaginary part and the 1-2 and 2-1 elements is
the covariance between the real and imaginary parts.
squeeze(Covariance(ky,ku,kf,:,:)) thus gives the covariance matrix of the
corresponding response.
The information about spectrum is optional. The format is as follows:
spec is a 3-D array of dimension ny-by-ny-by-Nf, such that spec(ky1,ky2,kf)
is the cross spectrum between the noise at output ky1 and the noise at output
ky2, at frequency Freqs(kf). When ky1=ky2 the (power) spectrum of the noise
at output ky1 is thus obtained. For a single output model, spec can be given as
a vector.
speccov is a 3-D array of dimension ny-by-ny-by-Nf, such that
speccov(ky1,ky1,kf) is the variance of the corresponding power spectrum.
Normally, no information is included about the covariance of the non-diagonal
spectrum elements.
If only SpectrumData is to be packaged in the idfrd object, set Response = [].
Creating idfrd from a Given Model
idfrd can also be computed from a given model mod (defined as any idmodel
object).
The default values of the frequencies in the discrete-time case are
Freqs = [1:128]'/128∗pi/Ts
where Ts is the sampling interval specified by mod and for the continuous-time
case
Freqs = logspace(log10(pi/Ts/100),log10(10*pi/ Ts),128)
where Ts is the sampling interval of the data from which the model was
estimated. If the model is not estimated, a simple default choice of Freqs is
made. In this case it may be necessary to supply the argument Freqs explicitly.
If mod has InputDelay different from zero, these are appended as phase lags,
and h will then have an InputDelay 0.
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idfrd
The estimated covariances are computed using the Gauss approximation
formula from the uncertainty information in mod. For models with complicated
parameter dependencies, numerical differentiation is applied. The step-sizes
for the numerical derivatives are determined by nuderst.
Frequency responses for submodels can be obtained by the standard
subreferencing: h = idfrd(m(2,3)). See idmodel. In particular, h=
idfrf(m('measured')) gives h that just contains the ResponseData (G) and
no spectra. Also h = idfrd(m('noise')) gives a h that just contains
SpectrumData.
The idfrd models can be graphed with bode, ffplot, and nyquist, which all
accept mixtures of idmodel and idfrd models as arguments. Note that spa and
etfe return their estimation results as idfrd objects.
idfrd
Properties
To summarize the properties of idfrd:
• ResponseData: A 3-D array of the complex-valued frequency response as
described above. For SISO system use Response(1,1,:) to obtain a vector of
the response data.
• Frequency: A column vector containing the frequencies as which the
responses are defined.
• CovarianceData: A 5-D array of the covariance matrices of the response data
as described above.
• SpectrumData: A 3-D array containing power spectra and cross spectra of
the output disturbances (noise) of the system.
• NoiseCovariance: A 3-D array containing the variances of the power
spectra, as explained above.
• Units: the unit of the frequency vector. Can assume the values ‘rad/s' and
'Hz'.
• Ts: A scalar denoting the sampling interval of the model whose frequency
response is stored. 'Ts' = 0 means a continuous-time model.
• Name: An optional name for the object
• InputName: A string or a cell array containing the names of the input
channels. It has as many entries as there are input channels.
• OutputName: Correspondingly for the output channels.
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idfrd
• InputUnit: The units in which the input channels are measured. It has the
same format as 'InputName'.
• OutputUnit: Correspondingly for the output channels.
• InputDelay: A row vector of length equal to the number of input channels.
Contains the delays from the input channels. These should thus be appended
as phase lags when the response is calculated. This is done automatically by
freqresp, bode, ffplot, and nyquist. Note that if the idfrd is calculated
form an idmodel, possible input delays in that model are converted to phase
lags, and InputDelay of the idfrd model is set to zero.
• Notes: An arbitrary field to store extra information and notes about the
object.
• UserData: An arbitrary field for any possible use.
• EstimationInfo: A structure that contains information about the estimation
process that is behind the frequency data. It contains the following fields:
- Status: Gives the status of the model, e.g., 'Not estimated'.
- Method: The identification routine that created the model.
- WindowSize: If the model was estimated by spa or etfe, the size of window
(input argument M) that was used.
- DataName: The name of the data set from which the model was estimated.
- DataLength: The length of this data set.
Note that all properties can be set or retrieved either by set/get or by
subscripts. Autofill applies to all properties and values, and these are case
insensitive:
h.ts = 0
loglog(h.fre,squeeze(h.spe(2,2,:)))
For a complete list of property values, use get(m). To see possible value
assignments, use set(m). See also idprops idfrd.
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idfrd
Subreferencing
The different channels of the idfrd are retrieved by subreferencing.
h(outputs,inputs)
h(2,3) thus contains the response data from input channel 3 to output channel
2, and, if applicable, the output spectrum data for output channel 2. The
channels can also be referred to by their names
h('power',{'voltage',''speed'}).
h('m')
contains the information for measured inputs only, that is, just ResponseData,
while
h('n')
('n' for 'noise') just contains SpectrumData.
Horizontal
Concatenation
Adding input channels
h = [h1,h2,...,hN]
creates an idfrd model h, with ResponseData containing of all the input
channels in h1,... hN. The output channels of hk must be the same as well as
the frequency vectors. SpectrumData will be ignored.
Vertical
Concatenation
Adding output channels
h = [h1;h2;... ;hN]
creates an idfrd model h with ResponseData containing all the output
channels in h1, h2,...,hN. The input channels of hk must all be the same, as
well as the frequency vectors. SpectrumData will also be appended for the new
outputs. The cross spectrum between output channels will then be set to zero.
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idfrd
Examples
Compare the results from spectral analysis and an ARMAX model.
m = armax(z,[2 2 2 1]);
g = spa(z)
bode(g,m)
Compute separate idfrd models, one containing g and the other the noise
spectrum.
g = idfrd(m('m'))
phi = idfrd(m('n'))
See Also
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bode, etfe, ffplot, freqresp, nyquist, spa
idgrey
4idgrey
Purpose
Package a greybox model structure defined by a user-written M-file into an
idgrey model.
Syntax
m = idgrey(MfileName,ParameterVector,CDmfile)
m = idgrey(MfileName,ParameterVector,CDmfile,FileArgument,Ts,...
'Property1',Value1,...,'PropertyN',ValueN)
Description
The function idgrey is used to create arbitrarily parameterized state-space
models as idgrey objects.
MfileName is the name of an M-file that defines how the state-space matrices
depend on the parameters to be estimated. The format of this M-file is given by
[A,B,C,D,K,X0] = mymfile(pars,Tsm,Auxarg)
and is further discussed below.
ParameterVector is a column vector of the nominal/initial parameters. Its
length must be equal to the number of free parameters in the model (that is,
the argument pars in the example below).
The argument CDmfile describes how the user-written M-file handles
continuous/discrete-time model. It takes the following values:
• CDmfile = 'cd': The M-file returns the continuous-time state-space
matrices when called with the argument Tsm=0. When called with a value
Tsm>0 the M-file returns the discrete-time state-space matrices, obtained by
sampling the continuous-time system with sampling interval Tsm. The M-file
must consequently in this case include the sampling procedure.
• CDmfile = 'c'. The M-file always returns the continuous-time state-space
matrices, no matter the value of Tsm. In this case the toolbox’s estimation
routines will provide the sampling when fitting the model to discrete-time
data.
• CDmfile='d'. The M-file always returns discrete-time state-space matrices,
that may or may not depend on Tsm.
The argument FileArgument corresponds to the auxiliary argument Auxarg in
the user-written M-file. It can be used to handle several variants of the model
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idgrey
structure, without having to edit the M-file. If it is not used, enter
FileArgument = []. (Default).
Ts denotes the sampling interval of the model. Its default value is Ts = 0, that
is, a continuous-time model.
The idgrey object is a child of idmodel. Therefore any idmodel properties can
be set as property name/property value pairs in the idgrey command. They can
of course also be set by the command set, or by subassignment, like
m.InputName = {'speed','voltage'}
m.FileArgument = 0.23
There are also two properties, DisturbanceModel and InitialState that can
be used to affect the parameterizations of K and X0, thus overriding the
outputs from the M-file. See below.
idgrey
Properties
To summarize, the properties of idgrey are the following ones:
• MfileName: The name of the user-written M-file. See below for details
• CDmfile: How this file handles continuous/discrete models, depending on its
second argument T.
- CDmfile = 'cd' means that the mfile returns the continuous time state
space model matrices when the argument T = 0, and the discrete time
model, obtained by sampling with sampling interval T when T > 0.
- CDmfile= 'c' means that the mfile always returns continuous time model
matrices, no matter the value of T.
- CDmfile = 'd' means that the mfile always returns discrete time model
matrices that may or may not depend on the value of T.
• FileArgument: Possible extra input arguments to the user-written M-file
• DisturbanceModel: affects the parameterization of the K-matrix. It can
assume the following values:
- 'Model': This is the default. It means that the K matrix obtained from the
user-written M-file is used.
- 'Estimate': The K-matrix is treated as unknown and its elements are
estimated as free parameters.
- 'Fixed': The K-matrix is fixed to a given value.
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idgrey
- 'Zero': The K-matrix is fixed to zero, thus producing an output error
model.
Note that in the three last cases the output K from the user written M-file is
ignored. The estimated/fixed value is stored internally and does not change
when the model is sampled/resampled/or converted to continuous time. Note
also that this estimated value is tailored only to the sampling interval of the
data.
• InitialState: affects the parameterization of the X0-vector. It assumes the
same values as DisturbanceModel, with analogous interpretations. In
addition InitialState can assume the value ‘Backcast’, with the same
interpretation as for an idss object.
• A, B, C, D, K, and X0: The state-space matrices. For idgrey models only
'K' and 'X0' can be set, the others only retrieved. The set 'K' and 'X0' are
relevant only when DisturbanceModel/InitialState are Estimate or
Fixed.
• dA, dB, dC, dD, dK, and dX0: The estimated standard deviations of the
state-space matrices. These cannot be set, only retrieved.
In addition, any idgrey object also has all the properties of idmodel. See
Algorithm Properties and the reference page for idmodel.
Note that all properties can be set/get either by these commands or by
subscripts. Autofill applies to all properties and values, and are case
insensitive.
m.fi = 10;
set(m,'search','gn')
p = roots(m.a)
For a complete list of property values, use get(m). To see possible value
assignments, use set(m). See also idprops and idgrey.
M-File Details
The model structure corresponds to the general linear state-space structure
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idgrey
x̃ ( t ) = A ( θ )x ( t ) + B ( θ )u ( t ) + K ( θ )e ( t )
x( 0 ) = x0 ( θ )
y ( t ) = C ( θ )x ( t ) + D ( θ )u ( t ) + e ( t )
Here x̃ ( t ) is the time derivative x· ( t ) for a continuous time model and x ( t + Ts )
for a discrete time model.
The matrices in this time-discrete model can be parameterized in an arbitrary
way by the vector θ . Write the format for the M-file as follows.
[A,B,C,D,K,x0] = mymfile(pars,T,Auxarg)
Here the vector pars contains the parameters θ , and the output arguments A,
B, C, D, K, and x0 are the matrices in the model description that correspond to
this value of the parameters and this value of the sampling interval T.
T is the sampling interval, and Auxarg is any variable of auxiliary variables
with which you want to work. (In that way you can change certain constants
and other aspects in the model structure without having to edit the M-file.)
Note that the two arguments T and Auxarg must be included in the function
head of the M-file, even if they are not utilized within the M-file.
Section “State-Space Models with Coupled Parameters: the idgrey Model” on
page 3-44 of the “Tutorial” chapter contains several examples of typical M-files
that define model structures.
A comment about CDmfile: If a continuous time model is sought, it is of course
most easy to let the mfile deliver just the continuous time model, i.e., have
CDmfile = 'c', and rely upon the toolbox’s routines for the proper sampling.
Similarly, if the underlying parameterization is indeed discrete time, it is
natural to deliver the discrete time model matrices, and let CDmfile = 'd'. If
the underlying parameterization is continuous, but you prefer for some reason
to do your own sampling inside the mfile, in accordance with the value of T,
then it is preferable to let your mfile deliver the continuous time model when
called with T = 0, that is, the alternative CMmfile = 'cd'. This will avoid
sampling, and then transforming back (using d2c) to find the continuous time
model.
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idgrey
Examples
Use the M-file 'mynoise' given in “Example 3.4: Parametrized Disturbance
Models” on page 3-47 in the of the “Tutorial” chapter to obtain a physical
parametrization of the Kalman gain.
mn = idgrey('mynoise',[0.1,-2,1,3,0.2]','d')
m = pem(z,mn)
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idinput
Purpose
4idinput
Generate signals, typically to be used as inputs for identification.
Syntax
u = idinput(N)
u = idinput(N,type,band,levels)
[u,freqs] = idinput(N,'sine',band,levels,sinedata)
Description
idinput generates input signals of different kinds, that are typically used for
identification purposes. u is returned as a matrix or column vector.
For further use in the toolbox it is recommended to create an iddata object
from u, indicating sampling time, input names, periodicity, and so on: u =
iddata([],u);
N determines the number of generated input data. If N is a scalar, u will be a
column vector with this number of rows.
N = [N nu] gives an input with nu input channels each of length N.
N = [P nu M] gives a periodic input with nu channels, each of length M*P and
periodic with period P.
Default is nu=1 and M =1.
type defines the type of input signal to be generated. This argument takes one
of the following values:
• type = ’rgs’: Gives a random, Gaussian signal.
• type = ’rbs’: Gives a random, binary signal.
• type = ’prbs’: Gives a pseudo-random, binary signal.
• type = ’sine’: Gives a signal which is a sum of sinusoids.
Default is type = ’rbs’.
The frequency contents of the signal is determined by the argument band. For
the choices type = ’rs’, ’rbs’, and ’sine’, this argument is a row-vector with two
entries
band = [wlow, whigh]
that determine the lower and upper bound of the pass-band. The frequencies
wlow and whigh are expressed in fractions of the Nyquist frequency. A white
noise character input is thus obtained for band = [0 1], which also is the
default value.
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idinput
For the choice type = ’prbs’
band = [0, B]
where B is such that the signal is constant over intervals of length 1/B (the
clock period). Also in this case the default is band = [0 1].
The argument levels defines the input level. It is a row vector
levels = [minu, maxu]
such that the signal u will always be between the values minu and maxu for the
choices type = ’rbs’, ’prbs’ and ’sine’. For type = ’rgs’, the signal level is such
that minu is the mean value of the signal, minus one standard deviation, while
maxu is the mean value plus one standard deviation. Gaussian white noise with
zero mean and variance one is thus obtained for levels = [-1, 1], which is
also the default value.
In the 'sine' case, the sinusoids are chosen from the frequency grid
freq = 2*pi*[1:Grid_Skip:fix(P/2)]/P intersected with pi*[band(1)
band(2)]
(for Grid_Skip, see below.) For multi-input signals, the different inputs use
different frequencies from this grid. An integer number of full periods is always
delivered. The selected frequencies are obtained as the second output
argument, freqs, where row ku of freqs contains the frequencies of input
number ku. The resulting signal is affected by a fifth input argument sinedata
sinedata = [No_of_Sinusoids, No_of_Trials, Grid_Skip]
meaning that No_of_Sinusoids is equally spread over the indicated band.
No_of_Trials (different, random, relative phases) are tried until the lowest
amplitude signal is found.
Default: sinedata = [10,10,1];
Grid-skip may be useful for controlling odd and even frequency multiples, e.g.,
to detect nonlinearities of various kinds.
Algorithm
Very simple algorithms are used. The frequency contents is achieved for 'rgs'
by an eighth order Butterworth, noncausal filter, using idfilt. This is quite
reliable. The same filter is used for the ’rbs’ case, before making the signal
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idinput
binary. This means that the frequency contents is not guaranteed to be precise
in this case.
For the ’sine’ case, the frequencies are selected to be equally spread over the
chosen grid, and each sinusoid is given a random phase. A number of trials are
made, and the phases that give the smallest signal amplitude are selected. The
amplitude is then scaled so as to satisfy the specifications of levels.
Reference
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See Söderström and Stoica (1989), Chapter C5.3. For a general discussion of
input signals, see Ljung (1999), Section 13.3.
idmodel
Purpose
4idmodel
Description
idmodel is an object that the user does not deal with directly. It contains all the
common properties of the model objects idarx, idgrey, idpoly, and idss,
which are returned by the different estimation routines.
Package all common model properties.
Basic Use
If you just estimate models from data, the model objects should be transparent.
All parametric estimation routines return idmodel results.
m = arx(Data,[2 2 1])
The model m contains all relevant information. Just typing m will give a brief
account of the model. present(m) also gives information about the
uncertainties of the estimated parameters. get(m) gives a complete list of
model properties.
Most of the interesting properties can be directly accessed by subreferencing.
m.a
m.da
See the property list obtained by get(m), as well as the property lists of
idgrey, idarx, idpoly, and idss in the “Command Reference” for more details
on this. See also idprops.
The characteristics of the model m can be directly examined and displayed by
commands like impulse, step, bode, nyquist, pzmap. The quality of the model
is assessed by commands like compare, and resid. If you have the Control
System Toolbox, just typing view(m) gives access to various display functions.
To extract state-space matrices, transfer function polynomials, etc., use the
commands
arxdata, polydata, tfdata, ssdata, zpkdata
and by idfrd and freqresp, the frequency response of the model can be
computed.
Creating and Modifying Model Objects
If you want to define a model to use, e.g., for simulating data, you need to use
the model creator functions:
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idmodel
• idarx, for multivariable arx models
• idgrey, for user-defined greybox state-space models
• idpoly, for single output polynomial models
• idss, for state-space models
Also, if you want to estimate a state-space model with a specific internal
parameterization, you need to create an idss model or a idgrey model. See the
respective reference pages for these functions.
Dealing with Input and Output Channels
For multivariable models, you construct submodels containing a subset of
inputs and outputs by simple subreferencing. The outputs and input channels
can be referenced according to
m(outputs,inputs)
Use colon (:) to denote all channels and the empty matrix ([ ]) to denote no
channels. The channels can be referenced by number or by name. For several
names, a cell array must be used.
m3 = m('position',{'power','speed'})
or
m3 = m(3,[1 4])
Thus m3 is the model obtained from m by looking at the transfer functions from
input numbers 1 and 4 (with input names 'power' and 'speed') to output
number 3 (with name position).
For a single output model m
m4 = m(inputs)
will select the corresponding input channels, and for a single input model
m5 = m(outputs)
will select the indicated output channels.
Subreferencing is quite useful; e.g., when a plot of just some channels is
desired.
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idmodel
The Noise Channels
The estimated models have two kinds of input channels: the measured inputs
u and the noise inputs e. For a general linear model m, we have
y ( t ) = G ( q )u ( t ) + H ( q )e ( t )
(4-1)
where u is the nu-dimensional vector of measured input channels and e is the
ny-dimensional vector of noise channels. The covariance matrix of e is given by
the property 'NoiseVariance'. Occasionally this matrix Λ will be written in
factored form
Λ = LL
T
This means that e can be written as
e = Lv
where v is white noise with identity covariance matrix (independent noise
sources with unit variances).
If m is a time series (nu = 0), G is empty and the model is given by
y ( t ) = H ( q )e ( t )
For the model m, the restriction to the transfer function matrix G is obtained by
m1 = m('measured') or just m1 = m('m')
Then e is set to 0 and H is removed.
Analogously
m2 = m('noise') or just m2 = m('n')
creates a time-series model m2 from m by ignoring the measured input. That is,
m2 describes the signal He.
For a system with measured inputs, bode, step, and many other
transformation and display functions just deal with the transfer function
matrix G. To obtain or graph the properties of the disturbance model H, it is
therefore important to make the transformations m('n'). For example,
bode(m('n'))
will plot the additive noise spectra according to the model m, while
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idmodel
bode(m)
just plots the frequency responses of G.
To study the noise contributions in more detail, it is useful to convert the noise
channels to measured channels, using the command noisecnv:
m3 = noisecnv(m)
This creates a model m3 with all input channels, both measured u and noise
sources e, being treated as measured signals,. That is, m3 is a model from u and
e to y, describing the transfer functions G and H. The information about the
variance of the innovations e is then lost. For example, studying the step
response from the noise channels, will then not take into consideration how
large the noise contributions actually are.
To include that information, e should first be normalized e = Lv , so that v
becomes white noise with an identity covariance matrix.
m4 = noisecnv(m,'Norm')
This will create a model m4 with u and v treated as measured signals.
y ( t ) = G ( q )u ( t ) + H ( q )Lv ( t ) = G HL u
v
For example, the step responses from v to y will now also reflect the typical size
of the disturbance influence, due to the scaling by L. In both these cases, the
previous noise sources, that have become regular inputs will automatically get
input names that are related to the corresponding output. The unnormalized
noise sources e have names like '[email protected]' (noise e at output channel ynam1),
while the normalized sources v are called '[email protected]'.
Retrieving Transfer Functions
The functions that retrieve transfer function properties, ssdata, tfdata, and
zpkdata will thus work as follows for a model (Equation 4-1) with measured
inputs: (fcn is any of ssdata, tfdata, or zpkdata.)
fcn(m) returns the properties of G (ny outputs and nu inputs)
fcn(m('n')) returns the properties of the transfer function H (ny outputs and
ny inputs)
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idmodel
fcn(noisec nv(m,'Norm')) returns the properties of the transfer function [G
HL} (ny outputs and ny+nu inputs). Analogously
m1 = m('n'). fcn(noisecnv(m1,'Norm'))
returns the properties of the transfer function HL (ny outputs and ny inputs).
If m is a time series model, fcn(m) returns the properties of H, while
fcn(noisecnv(m,'Norm'))
returns the properties of HL.
Note that the estimated covariance matrix NoiseVariance itself is uncertain.
This means that the uncertainty information about H is different from that of
HL.
idmodel
Properties
In the list below, ny is the number of output channels, and nu is the number of
input channels:
• Name: An optional name for the data set. An arbitrary string.
• OutputName, InputName: Cell arrays of length ny-by-1 and nu-by-1
containing the names of the output and input channels. For estimated
models, these are inherited from the data. If not specified, they will be given
default names: {'y1','y2',...} and {'u1','u2',...}.
• OutputUnit, InputUnit: Cell arrays of length ny-by-1 and nu-by-1 containing
the units of the output and input channels. Inherited from data for estimated
models.
• TimeUnit: The unit for the sampling interval.
• Ts: Sampling interval. A non-negative scalar. Ts = 0 denotes a
continuous-time model. Note that changing just Ts will not recompute the
model parameters. Use c2d and d2c for recomputing the model to other
sampling intervals.
• ParameterVector: The vector of adjustable parameters in the model
structure. Initial/nominal values or estimated values, depending on the
status of the model. A column vector.
• PName: The names of the parameters. A cell array of the length of the
parameter vector. If not specified, it will contain empty strings. See also
setpname.
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idmodel
• CovarianceMatrix: The estimated covariance matrix of the parameter
vector. For a nonestimated model this is the empty matrix. For state-space
models in the 'Free' parameterization the covariance matrix is also the
empty matrix, since the individual matrix elements are not identifiable then.
Instead, in this case, the covariance information is hidden (in the hidden
property 'Utility') and retrieved by the relevant functions when
necessary. Setting CovarianceMatrix to 'None' will inhibit calculation of
covariance and uncertainty information. This may save substantial time for
certain models. See “No Covariance” on page 3-92 in the “Tutorial” chapter.
• NoiseVariance: The covariance matrix of the noise source e. An ny-by-ny
matrix.
• InputDelay: A vector of size nu-by-1, containing the input delay from each
input channel. For a continuous-time model (Ts = 0) the delay is measured
in TimeUnit, while for discrete-time models (Ts > 0) the delay is measured
as the number of samples. Note the difference between InputDelay and nk
(which is a property of idarx, idss, and idpoly). 'Nk' is a model structure
property that tells the model structure to include such an input delay. In that
case, the corresponding state-space matrices and polynomials will explicitly
contain Nk input delays. The property InputDelay, on the other hand, is an
information that in addition to the model as defined, the inputs should be
shifted by the given amount. InputDelay is used by sim and the estimation
routines to shift the input data. When computing frequency responses, the
InputDelay is also respected. Note that InputDelay can be both positive and
negative.
• Algorithm: See the reference page for Algorithm Properties.
• EstimationInfo: See the reference page for EstimationInfo.
• Notes: An arbitrary field to store extra information and notes about the
object.
• UserData: An arbitrary field for any possible use.
Note All properties can be set or retrieved either by these commands or by
subscripts. Autofill applies to all properties and values, and is case
insensitive.
4-82
idmodel
For a complete list of property values, use get(m). To see possible value
assignments, use set(m).
Subreferencing
The outputs and input channels can be referenced according to
m(outputs,inputs)
Use colon (:) to denote all channels and the empty matrix ([ ]) to denote no
channels. The channels can be referenced by number or by name. For several
names, a cell array must be used.
m2 = m('y3',{'u1','u4'})
m3 = m(3,[1 4])
For a single output model m
m4 = m(inputs)
will select the corresponding input channels, and for a single input model
m5 = m(outputs)
will select the indicated output channels.
The string 'measured' (or any abbreviation like 'm') means the measured
input channels.
m4 = m(3,'m')
m('m') is the same as m(:,'m')
Similarily the string 'noise' (or any abbreviation) refers to the noise input
channels. See above under “The Noise Channels” for more details.
Horizontal
Concatenation
Adding input channels
m = [m1,m2,...,mN]
creates an idmodel object m, consisting of all the input channels in m1,... mN.
The output channels of mk must be the same.
Vertical
Concatenation
Adding output channels
m = [m1;m2;... ;mN]
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idmodel
creates an idmodel object m consisting of all the output channels in m1, m2,
..mN. The input channels of mk must all be the same.
Online Help
Functions
See idhelp idprops idmodel, help idmodel/subsref, help idmodel/
subsasgn, help idmodel/horzcat, and help idmodel/vertcat.
See Also
noisecnv, nkshift, plot (iddata), size
4-84
idmodred
Purpose
4idmodred
Reduce the order of a model (requires the Control System Toolbox).
Syntax
MRED = idmodred(M)
MRED = idmodred(M,ORDER,'DisturbanceModel','None')
Description
This function reduces the order of any model M given as an idmodel object. The
resulting reduced order model MRED is an idss model.
The function requires several routines in the Control System Toolbox.
ORDER: The desired order (dimension of the state-space representation). If
ORDER = [], which is the default, a plot will show how the diagonal elements
of the observability and controllability Gramians of a balanced realization
decay with the order of the representation. You will then be prompted to select
an order based on this plot. The idea is that such a small element will have a
negligible influence on the input-output behavior of the model. It is thus
suggested that an order is chosen, such that only large elements in these
matrices are retained.
'DisturbanceModel': If the property DisturbanceModel is set to 'None', then
an Output- Error model MRED is produced, that is, one with the Kalman gain
equal to zero (see Equation (Equation 3-23) in the “Tutorial” chapter).
Otherwise (default), also the disturbance model is reduced.
The function will recognize whether M is a continuous- or discrete-time model
and perform the reduction accordingly. The resulting model MRED will be of the
same kind in this respect as M.
Algorithm
The functions balreal and modred from the Control System Toolbox are used.
The plot, in case ORDER = [], shows the vector g as returned from balreal.
Examples
Build a high order multivariable ARX model, reduce its order to 3 and compare
the frequency responses of the original and reduced models.
M = arx(data,[4∗ones(3,3),4∗ones(3,2),ones(3,2)]);
MRED = idmodred(M,3);
bode(M,MRED)
Use the reduced order model as initial condition for a third order state-space
model.
M2= pem(data,MRED);
4-85
idpoly
Purpose
4idpoly
Construct an idpoly model for input-output models.
Syntax
m = idpoly(A,B)
m = idpoly(A,B,C,D,F,NoiseVariance,Ts)
m = idpoly(A,B,C,D,F,NoiseVariance,Ts,'Property1',Value1,...
'PropertyN',ValueN)
m = idpoly(mi)
Description
idpoly creates a model object containing parameters that describe the general
multi-input-single-output model structure.
B1( q )
B nu ( q )
C( q)
A ( q )y ( t ) = --------------- u 1 ( t – nk 1 ) + ------------------- u nu ( t – nk nu ) + ------------- e ( t )
D(q)
F1 ( q )
F nu ( q )
A, B, C, D, and F specify the polynomial coefficients.
For single-input systems, these are all row vectors in the standard MATLAB
format.
A = [1 a1 a2 ...
ana]
consequently describes
A ( q ) = 1 + a1 q
–1
+ … + a na q
– na
A, C, D, and F all start with 1, while B contains leading zeros to indicate the
delays. See ““Polynomial Representation of Transfer Functions” on page 3-11
in the “Tutorial” chapter.
For multi-input systems, B and F are matrices with one row for each input.
For time series, B and F are entered as empty matrices.
B = [];
F = [];
NoiseVariance is the variance of the white noise sequence e ( t ) , while Ts is the
sampling interval.
Trailing arguments C, D, F, NoiseVariance, and Ts can be omitted, in which
case they are taken as 1. (If B=[], then F is taken as [].) The property name/
property value pairs can start directly after B.
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idpoly
Ts = 0 means that the model is a continuous-time one. Then the interpretation
of the arguments is that
A = [1 2 3 4]
3
2
corresponds to the polynomial s + 2s + 3s + 4 in the Laplace variable s, and
so on. For continuous-time systems NoiseVariance indicates the level of the
spectral density of the innovations. A sampled version of the model has the
innovations variance NoiseVariance/Ts, where Ts is the sampling interval.
The continuous-time model must have a white noise component in its
disturbance description. See the section “Spectrum Normalization and the
Sampling Interval” on page 3-94 in the “Tutorial” chapter.
For discrete-time models (Ts>0), note the following: idpoly strips any trailing
zeros from the polynomials when determining the orders. It also strips leading
zeros from the B polynomial to determine the delays. Keep this in mind when
you use idpoly and polydata to modify earlier estimates to serve as initial
conditions for estimating new structures. See the section “Initial Parameter
Values” on page 3-90 in the “Tutorial” chapter.
idpoly can also take any single-output idmodel or LTI-object mi as input
argument. If an LTI-system has an input group with name ‘Noise’, these inputs
will be interpreted as white noise with unit variance, and the noise model of
the idpoly model will be computed accordingly.
Idpoly
Properties
The properties of the idpoly object can be summarized as follows:
• na, nb, nc, nd, nf, nk: The orders and delays of the polynomials. Integers
or row vectors of integers.
• a, b, c, d, f: The polynomials, described by row vectors and matrices as
detailed above.
• da, db, dc, dd, df: The estimated standard deviation of the polynomials.
Cannot be set.
• InitialState: How to deal with the initial conditions that are required to
compute the prediction of the output: Possible values
- 'Estimate': The necessary initial states are estimated from data as extra
parameters
- 'Backcast': The necessary initial states are estimated by a backcasting
(backwards filtering) process, described in Knudsen (1994)
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idpoly
- 'Zero': all initial states are taken as zero
- 'Auto': An automatic choice between the above is made, guided by the
data.
In addition, any idpoly object also has all the properties of idmodel. See
idmodel properties and Algorithm Properties.
Note that all properties can be set or retrieved either by set/get or by
subscripts. Autofill applies to all properties and values, and are these case
insensitive.
m.a=[1 -1.5 0.7];
set(m,'ini','b')
p = roots(m.a)
For a complete list of property values, use get(m). To see possible value
assignments, use set(m). See also idprops idpoly.
Examples
To create a system of ARMAX, type
A = [1 -1.5 0.7];
B = [0 1 0.5];
C = [1 -1 0.2];
m0 = idpoly(A,B,C);
This gives a system with one delay (nk = 1).
Create the continuous-time model
1
s+3
y ( t ) = -------------------- u 1 ( t ) + ---------------------------- u 2 ( t ) + e ( t )
2
s(s + 1 )
s + 2s + 4
Sample it with T=0.1 and then simulate it without noise.
B=[0 1;1 3];
F=[1 1 0;1 2 4]
m = idpoly(1,B,1,1,F,1,0)
md = c2d(m,0.1)
y = sim(md,[u1 u2]);
4-88
idpoly
Note that the continuous time model will automatically be sampled to the
sampling interval of the data, when simulated, so the above is also achieved by
u = iddata([],[u1 u2],0.1)
y = sim(m,u)
See Also
sim, idss
References
See Ljung (1999) Section 4.2 for the model structure family.
See
T. Knudsen (1994): A new method for estimating ARMAX models. In Proc. 10th
IFAC Symposium on System Identification, pp 611-617. Copenhagen, Denmark
for the backcast method.
4-89
idss
Purpose
4idss
Package state-space parameterizations into an idss model.
Syntax
m = idss(A,B,C,D)
m = idss(A,B,C,D,K,x0,Ts,'Property1',Value1,...,'PropertyN',ValueN)
mss = idss(m1)
Description
The function idss is used to construct state-space model structures with
various parameterizations. It is a complement to idgrey and deals with
parameterizations that do not require the user to write a special M-file. Instead
it covers parameterizations that either a completely 'Free', that is, all
parameters in the A, B and C matrices can be adjusted freely, or 'Canonical',
meaning that the matrices are parameterized as canonical forms. The
parameterization can also be 'Structured', which means that certain
elements in the state-space matrices are free to be adjusted, while other are
fixed. This is explained below.
Ts is the sampling interval. Ts = 0 means a continuous time model.
The idss object m describes state-space models in innovations form, of the
following kind.
x̃ ( t ) = A ( θ )x ( t ) + B ( θ )u ( t ) + K ( θ )e ( t )
x( 0 ) = x0 ( θ )
y ( t ) = C ( θ )x ( t ) + D ( θ )u ( t ) + e ( t )
Here x̃ ( t ) is the time derivative x· ( t ) for a continuous time model and x ( t + Ts )
for a discrete time model.
The model m will contain information both about the nominal/initial values of
the A, B, C, D, K, and X0-matrices and about how these matrices are
parameterized by the parameter vector θ (to be estimated).
The nominal model is defined by idss(A,B,C,D,K,X0). If K and X0 are omitted
they are taken as zero matrices of appropriate dimensions.
Defining an idss object from a given model
mss = idss(m1)
constructs an idss model from any idmodel or LTI-system m1.
4-90
idss
If m1 is an LTI system (ss, tf or zpk) which has no InputGroup called
'Noise', the corresponding state-space matrices A,B,C,D are used to define the
idss object. The Kalman gain K is then set to zero.
If the LTI-system has an InputGroup called 'Noise', these inputs will be
interpreted as white noise with a covariance matrix equal to the identity
matrix. The corresponding Kalman gain and noise variance is then computed
and entered into the idss model together with A,B,C, and D.
Parameterizations
There are several different ways to define the parameterization of the
state-space matrices. The parameterization will determine which parameters
can be adjusted to data by the parameter estimation routine pem:
• Free Black-Box parameterizations: This is the default situation and
corresponds to letting all parameters in A, B, and C be freely adjustable. This
is obtained by setting the property 'SSParameterization' = 'Free'. The
parameterizations of D, K, and X0 are then determined by the following
properties:
- 'nk': A row vector of the same length as the number of inputs. The ku-th
element is the delay from input channel no ku. nk =[0,...,0], thus means
that there is no delay from any of the inputs, and that consequently all
elements of the D matrix should be estimated. nk =[1,...,1] means that
there is a delay of 1 from each input, so that the D matrix is fixed to be zero.
- 'DisturbanceModel': This property affects the parametrization of K and
can assume the values:
'Estimate' which means that all elements of the K matrix are to be
estimated.
'None': all elements of K are fixed to zero.
'Fixed': all elements of K are fixed to their nominal/initial values.
- 'InitialState': This properly affects the parameterization of X0 and can
assume the following values:
'Auto': An automatic choice of the below is made, depending on data
(default).
'Estimate': All elements of X0 are to be estimated.
'Zero': All elements of X0 are fixed to zero.
4-91
idss
'Fixed': All elements of X0 are fixed to their nominal/initial values.
'Backcast': The vector X0 is adjusted, during the parameter estimation
step, to a suitable value, but it is not stored as an estimation result.
• Canonical Black-Box parameterizations: This is obtained by setting the
property 'SSParameterization' = 'Canonical'. The matrices A, B and C
are then parameterized as an observer canonical form, which means that ny
(= the number of output channels) rows of A are fully parameterized while
the others contain 0’s and 1’s in a certain pattern. The C matrix is built up
of 0’s and 1’s while the B matrix is fully parameterized. See Equation(A.16)
in Ljung(1999) for details. The exact form of the parameterization is affected
by the property 'CanonicalIndices'. The default value 'Auto' is a good
choice. The parameterization of the D, K and X0-matrices in this case is
determined by the properties 'nk', 'DisturbanceModel' and
'InitialState' exactly as above.
• Arbitrarily structured parameterizations: To cover the general case
where arbitrary elements of the state-space matrices may be fixed and others
be freely adjusted, corresponds to the case 'SSParameterization' =
'Structured'. Then the parameterization is determined by the idss
properties As, Bs, Cs, Ds, Ks, and X0s. These are the structure matrices
that are “shadows” of the state-space matrices, so that an element in these
matrices that is equal to NaN indicates a freely adjustable parameter, while
a numerical value in these matrices indicates that the corresponding system
matrix element is fixed (nonadjustable) to this value.
See the Examples below.
idss Properties
To summarize the properties of the idss object we have:
• SSParameterization with possible values
- 'Free': Means that all parameters in A,B and C are freely adjustable and
the parameterizations of D, K and X0 depend on the properties 'nk',
'DisturbanceModel' and 'InitialState'
- 'Canonical': Means that A and C are parameterized as an observer
canonical form. The details of this parameterization depends on the
property 'CanonicalIndices'. The B-matrix is always fully
parameterized, and the parameterizations of D, K, and X0 depend on the
properties 'nk', 'DisturbanceModel', and 'InitialState'.
4-92
idss
- 'Structured': Means that the parametrization is determined by the
properties (the structure matrices) 'As', 'Bs', 'Cs', 'Ds', 'Ks', and
'X0s'. A NaN in any position in these matrices denotes a freely adjustable
parameter and a numeric value denotes a fixed and nonadjustable
parameter.
• nk: A row vector with as many entries as the number of input channels. The
entry number k denotes the time delay from input number k to y(t). This
property is relevant only for 'Free' and 'Canonical' parameterizations. If
any delay is larger than 1, the structure of the A, B, and C matrices will accommodate this delay, at the price of a higher order model.
• DisturbanceModel with possible values:
- 'Estimate': Means that the K matrix is fully parameterized.
- 'None': Means that the K matrix is fixed to zero. This gives a so-called
Output-Error model, since the model output depends on past inputs only.
- 'Fixed': Means that the K matrix is fixed to the current nominal values
• InitialState with possible values:
- 'Estimate': Means that X0 is fully parameterized.
- 'Zero': Means that X0 is fixed to zero.
- 'Fixed': Means that X0 is fixed to the current nominal value.
- 'Backcast': The value of X0 is estimated by the identification routines as
the best fit to data, but it is not stored.
- 'Auto': Gives an automatic, and data-dependent choice between
'Estimate', 'Zero' and 'Backcast'.
• A, B, C, D, K, and X0: The state-space matrices that can be set and
retrieved at any time. These contain both fixed values and estimated/
nominal values.
• dA, dB, dC, dD, dK, and dX0: The estimated standard deviations of the
state-space matrices. These cannot be set, only retrieved. Note that these are
not defined for an idss model with 'Free' SSParameterization. You can
then convert the parameterization to 'Canonical' and study the
uncertainties of the matrix elements in that form.
• As, Bs, Cs, Ds, Ks, and X0s: These are the structure matrices that have
the same sizes as A, B, C etc. and show the freely adjustable parameters as
NaN in the corresponding position. These properties are used to define the
model structure for 'SSParameterization' = 'Structured'. They are
4-93
idss
however always defined and can be studied also for the other
parametrizations.
• CanonicalIndices: Determines the details of the canonical
parameterization. It is a row vector of integers with as many entries as there
are outputs. They sum up to the system order. This is the so-called
pseudo-canonical multi-index, with an exact definition, e.g., on page 132 in
Ljung (1999). A good default choice is 'Auto'. This property is relevant only
for the canonical parameterization case. Note however, that for 'Free'
parameterizations, the estimation algorithms also store a canonically
parameterized model, to handle the model uncertainty.
In addition to these properties, idss objects also have all the properties of the
idmodel object. See idmodel properties, Algorithm Properties, and
EstimationInfo.
Note that all properties can be set and retrieved either by set/get or by
subscripts. Autofill applies to all properties and values, and are case
insensitive.
m.ss='can'
set(m,'ini','z')
p = eig(m.a)
For a complete list of property values, use get(m). To see possible value
assignments, use set(m). See also idprops idss.
Examples
Define a continuous-time model structure corresponding to
θ1 0
θ
x· =
x+ 3 u
θ4
0 θ2
y = 1 1 x+e
4-94
idss
with initial values
– 0.2
θ = – 0.3
2
:
4
and estimate the free parameters
A = [-0.2, 0; 0, -0.3]; B = [2;4]; C=[1, 1]; D = 0
m0 = idss(A,B,C,D);
m0.As = [NaN,0;0,NaN];
m0.Bs = [NaN;NaN];
m0.Cs = [1,1];
m0.Ts = 0;
m = pem(z,m0);
Estimate a model in free parameterization. Convert it to continuous time, then
convert it to canonical form and continue to fit this model to data.
m1 = n4sid(data,3);
m1 = d2c(m1);
m1.ss ='can';
m = pem(data,m1);
All of this can be done at once by
m = pem(data,3,'ss','can','ts',0)
See Also
n4sid, pem
4-95
impulse
Purpose
Syntax
Description
4impulse
Estimate/compute/display impulse response.
impulse(m)
impulse(data)
impulse(data,'sd',sd,'pw',na,Time)
impulse(m,'sd',sd,Time)
impulse(m1,m2,...,dat1, ...,mN,Time,'sd',sd)
impulse(m1,'PlotStyle1',m2,'PlotStyle2',...,dat1,'PlotStylek',...,
mN,'PlotStyleN',Time,'sd',sd)
[y,t,ysd] = impulse(m)
mod = impulse(data)
impulse can be applied both to idmodels and to iddata sets, as well as to any
mixture.
For a discrete time idmodel m, the impulse response y and, when required, its
estimated standard deviation ysd, is computed using sim. When called with
output arguments, y, ysd and the time vector t are returned. When impulse is
called without output arguments, a plot of the impulse response is shown. If sd
is given a value larger than zero, a confidence region around zero is drawn. It
corresponds to the confidence of sd standard deviations. In the plots, the
impulse is inversely scaled with the sampling interval, so that it has the same
energy regardless of the sampling.
Adding an argument 'fill' among the input arguments gives an uncertainty
region marked by a filled area, rather than by dash-dotted lines.
The start time T1 and the end time T2 can be specified by Time= [T1 T2]. If T1
is not given, it is set to -T2/4. The negative time lags (the impulse is always
assumed to occur at time 0) show possible feedback effects in the data, when
the impulse is estimated directly from data. If Time is not specified, a default
value is used.
For an iddata set data, impulse(data) estimates a high order, noncausal FIR
model after first having prefiltered the data so that the input is “as white as
possible.” The impulse response of this FIR model and, when asked for, its
confidence region is then plotted. When called with an output argument,
impulse, in the iddata case, returns this FIR model, stored as an idarx
model.The order of the prewhitening filter can be specified as na. The default
value is na = 10.
4-96
impulse
Any number and any mixture of models and data sets can be used as input
arguments. The responses are plotted with each input/output channel (as
defined by the models’ and data sets’ InputName and OutputName) as a
separate plot. Colors, linestyles, and marks can be defined by PlotStyle
values. These are the same as for the regular plot command, like
impulse(m1,'b-*',m2,'y--',m3,'g')
The noise input channels in m are treated as follows: Consider a model m with
both measured input channels u (nu channels) and noise channels e (ny
channels) with covariance matrix Λ
y = Gu + He
cov ( e ) = Λ = LL′
where L is a lower triangular matrix. Note that m.NoiseVariance = Λ . The
model can also be described with unit variance, normalized noise source v:
y = Gu + HLv
cov ( v ) = I
• impulse(m) plots the impulse response of the transfer function G.
• impulse(m('n')) plots the impulse response of the transfer function H. (ny
inputs and ny outputs).The input channels have names [email protected], where
yname is the name of the corresponding output.
• If m is a time series, that is nu = 0, impulse(m) plots the impulse response of
the transfer function H.
• impulse(noisecnv(m)) plots the impulse response of the transfer function
[G H] (nu+ny inputs and ny outputs). The noise input channels have names
[email protected], where yname is the name of the corresponding output.
• impulse(noisecnv(m,'norm')) plots the impulse response of the transfer
function [G HL] (nu+ny inputs and ny outputs. The noise input channels
have names [email protected], where yname is the name of the corresponding output.
Arguments
If impulse is called with a single idmodel m, the output argument y is a 3-D
array of dimension Nt-by-ny-by-nu. Here Nt is the length of the time vector t,
ny is the number of output channels and nu is the number of input channels.
Thus y(:,ky,ku) is the response in output ky to an impulse in the ku-th input
channel.
4-97
impulse
ysd has the same dimensions as y and contains the standard deviations of y.
If impulse is called with an output argument and a single data set in the input
arguments, the output is returned as an idarx model mod containing the high
order FIR model, and its uncertainty. By calling impulse with mod, the
responses can be displayed and returned without having to redo the
estimation.
Example
impulse(data,'sd',3) estimates and plots the impulse response
mod = impulse(data)
impulse(mod,'sd',3)
See Also
4-98
cra, step
init
Purpose
4init
Set initial values for the parameters to be estimated.
Syntax
m = init(m0)
m = init(m0,R,pars,sp)
Description
This function randomizes initial parameter estimates for model structures m0
for any idmodel type. m is the same model structure as m0, but with a different
nominal parameter vector. This vector is used as the initial estimate by pem.
The parameters are randomized around pars with variances given by the row
vector R. Parameter number k is randomized as pars(k) + e*sqrt(R(k)),
where e is a normal random variable with zero mean and a variance of 1. The
default value of R is all ones, and the default value of pars is the nominal
parameter vector in m0.
Only models that give stable predictors are accepted. If sp = 'b', only models
that are both stable and have stable predictors are accepted.
sp = 's' requires stability only of the model, and sp = 'p' requires stability
only of the predictor. Sp = 'p' is the default.
Sufficiently free parameterizations can be stabilized by direct means without
any random search. To just stabilize such an initial model, set R =0. With R>0
randomization is also done.
For model structures where a random search is necessary to find a stable
model/predictor, a maximum of 100 trials are made by init. It may be difficult
to find a stable predictor for high order systems just by trial and error.
See Also
idss, n4sid, pem
4-99
ivar
Purpose
4ivar
Estimate the parameters of an AR model using an approximately optimal
choice of instrumental variable procedure.
Syntax
m = ivar(y,na)
m = ivar(y,na,nc,maxsize)
Description
The parameters of an AR model structure
A ( q )y ( t ) = v ( t )
are estimated using the instrumental variable method. y is the signal to be
modeled, entered as an iddata object (outputs only). na is the order of the A
polynomial (the number of A parameters). The resulting estimate is returned
as an idpoly model m. The routine is for scalar signals only.
In the above model, v ( t ) is an arbitrary process, assumed to be a moving
average process of order nc, possibly time varying. (Default is nc = na.)
Instruments are chosen as appropriately filtered outputs, delayed nc steps.
The optional argument maxsize is explained under Algorithm Properties.
Examples
Compare spectra for sinusoids in noise, estimated by the IV method and
estimated by the forward-backward least-squares method.
y = iddata(sin([1:500]'∗1.2) + sin([1:500]'∗1.5) +
0.2∗randn(500,1),[]);
miv = ivar(y,4);
mls = ar(y,4);
bode(miv,mls)
See Also
ar, etfe, spa
References
Stoica, P. et al., Optimal Instrumental variable estimates of the AR-parameters
of an ARMA process, IEEE Trans. Autom. Control, Vol AC-30, 1985, pp.
1066-1074.
4-100
ivstruc
Purpose
4ivstruc
Compute fit between simulated and measured output for a group of model
structures.
Syntax
v = ivstruc(ze,zv,NN)
v = ivstruc(ze,zv,NN,p,maxsize)
Description
NN is a matrix that defines a number of different structures of the ARX type.
Each row of NN is of the form
nn = [na nb nk]
with the same interpretation as described for arx. See struc for easy
generation of typical NN matrices for single-input systems.
Each of ze and zv are iddata objects containing output-input data. Models for
each model structure defined in NN are estimated using the instrumental
variable (IV) method on data set ze. The estimated models are simulated using
the inputs from data set zv. The normalized quadratic fit between the
simulated output and the measured output in zv is formed and returned in v.
The rows below the first row in v are the transpose of NN, and the last row
contains the logarithms of the condition numbers of the IV matrix
∑ ς ( t )ϕ
T
(t)
A large condition number indicates that the structure is of unnecessarily high
order (see page 498 in Ljung (1999)).
The information in v is best analyzed using selstruc.
If p is equal to zero, the computation of condition numbers is suppressed. For
the use of maxsize, see Algorithm Properties.
The routine is for single-output systems only.
Note The IV method used does not guarantee that the obtained models are
stable. The output-error fit calculated in v may then be misleading.
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ivstruc
Examples
Compare the effect of different orders and delays, using the same data set for
both the estimation and validation.
v = ivstruc(z,z,struc(1:3,1:2,2:4));
nn = selstruc(v)
m = iv4(z,nn);
Algorithm
A maximum order ARX model is computed using the least-squares method.
Instruments are generated by filtering the input(s) through this model. The
models are subsequently obtained by operating on submatrices in the
corresponding large IV matrix.
See Also
arxstruc, iv4, n4sid, selstruc, struc
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ivx
Purpose
4ivx
Estimate the parameters of an ARX model using the instrumental variable (IV)
method with arbitrary instruments.
Syntax
m = ivx(data,orders,x)
m = ivx(data,orders,x,maxsize)
Description
ivx is a routine analogous to the iv4 routine, except that you can use arbitrary
instruments. These are contained in the matrix x. Make this the same size as
the output, data.y. In particular, if data contains several experiments x must
be a cell array with one matrix/vector for each experiment. The instruments
used are then analogous to the regression vector itself, except that y is replaced
by x.
Note that ivx does not return any estimated covariance matrix for m, since that
requires additional information. m is returned as an idpoly object for single
output systems and as an idarx object for multi-output systems.
Use iv4 as the basic IV routine for ARX model structures. The main interest
in ivx lies in its use for nonstandard situations; for example when there is
feedback present in the data, or when other instruments need to be tried out.
Note that there is also an IV version that automatically generates instruments
from certain filters you define (type help iv).
See Also
iv4, ivar
References
Ljung (1999), page 222.
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iv4
Purpose
4iv4
Estimate the parameters of an ARX model using an approximately optimal
four-stage instrumental variable (IV) procedure.
Syntax
m = iv4(data,orders)
m = iv4(data,'na',na,'nb',nb,'nk',nk)
m= iv4(data,orders,'Property1',Value1,...,'PropertyN',ValueN)
Description
This routine is an alternative to arx and the use of the arguments are entirely
analogous to the arx function. The main difference is that the procedure is not
sensitive to the color of the noise term e ( t ) in the model equation.
For an interpretation of the loss function (innovations covariance matrix),
consult “Interpretation of the Loss Function” on page 3-97 in the “Tutorial”
chapter.
Examples
Here is an example of a two-input, one-output system with different delays on
the inputs u 1 and u 2 .
z = iddata(y, [u1 u2]);
nb = [2 2];
nk = [0 2];
m= iv4(z,[2 nb nk]);
Algorithm
The first stage uses the arx function. The resulting model generates the
instruments for a second-stage IV estimate. The residuals obtained from this
model are modeled as a high-order AR model. At the fourth stage, the
input-output data are filtered through this AR model and then subjected to the
IV function with the same instrument-filters as in the second stage.
For the multi-output case, optimal instruments are obtained only if the noise
sources at the different outputs have the same color. The estimates obtained
with the routine are reasonably accurate though even in other cases.
See Also
arx, oe
References
Ljung (1999), equations (15.21)-(15.26).
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LTI Commands
4LTI Commands
Purpose
To allow direct calls to typical LTI commands from idmodel objects. The
Control System Toolbox is required for these commands.
Syntax
append, augstate, balreal, canon, d2d, feedback, inv, minreal,
modred, norm, parallel, series, ss2ss
Description
If you have the Control System Toolbox, most of the relevant LTI-commands,
as listed under Syntax, can be directly applied to any idmodel (idarx, idgrey,
idpoly, idss). You can also use the overloaded operations +, -, and *. The same
operations are performed and the result is delivered as an idmodel. The
original covariance information is however lost, most of the time.
Example
You have two more or less identical processes connected in series. Estimate a
model for one of them, and use that to form an initial estimate for a model of
the connected process.
m = pem(data) % data concerns one of the processes
m2 = pem(data2,m*m) % data2 are from the whole connected process
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merge (iddata)
Purpose
4merge (iddata)
Merge different data sets into one iddata object.
Syntax
dat = merge(dat1,dat2,....,datN)
Description
dat collects the data sets in dat1,.. datN into one iddata object, with several
experiments. The number of experiments in dat will the sum of the number of
experiments in datk. For the merging to be allowed a number of conditions
must be satisfied:
• All of datk must have the same number of input channels, and the
InputNames must be the same.
• All of datk must have the same number of output channels, and the
OutputNames must be the same. If some input or output channel is lacking in
one experiment, it can be replaced by a vector of NaN’s to conform with these
rules.
• If the ExperimentNames of datk have been specified to something else than
the default 'Exp1', 'Exp2', etc., they must all be unique. If default names
overlap, they will be modified, so that dat will have a unique list of
ExperimentNames.
The sampling intervals, the number of observations, and the input properties
(Period, InterSample) may be different in the different experiments.
The individual experiments can be retrieved by the command getexp. The can
also be retrieved by subreferencing with a fourth index:.
dat1 = dat(:,:,:,ExperimentNumber) or dat1 =
dat(:,:,:,ExperimentName)
Storing multiple experiments as one iddata object may be very useful to
handle experimental data that have been collected on different occasions, or
when a data set has been split up to remove “bad” portions of the data. All the
toolbox’s routines accept multiple experiment data.
Example
Bad portions of data have been detected around sample 500 and between
samples 720 - 730. Cut out these bad portions and form a multiple experiment
data set that can be used to estimate models, without the bad data destroying
the estimate.
dat = merge(dat(1:498),dat(502:719),dat(719:1000))
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merge (iddata)
m = pem(dat)
Use the first two parts to estimate the model and the third one for validation.
m = pem(dat{1:2});
compare(dat{3},m)
See also iddemo #8
See Also
iddata, getexp
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merge (idmodel)
Purpose
4merge (idmodel)
Merge different models into one.
Syntax
m = merge(m1,m2,....,mN)
[m,tv] = merge(m1,m2)
Description
The models m1,m2,...,mN must all be of the same structure, just differing in
parameter values and covariance matrices. m is then the merged model, where
the parameter vector is a statistically weighted mean (using the covariance
matrices to determine the weights) of the parameters of mk.
When two models are merged,
[m, tv] = merge(m1,m2)
2
returns a test variable tv. It is χ distributed with n degrees of freedom, if the
parameters of m1 and m2 have the same means. Here n is the length of the
parameter vector. A large value of tv thus indicates that it might be
questionable to merge the models.
Merging models is an alternative to merging data sets, and estimating a model
for the merged data. Consequently
m1 = arx(z1,[2 3 4]);
m2 = arx(z2,[2 3 4]);
ma = merge(m1,m2);
and
mb = arx(merge(z1,z2),[2 3 4]);
lead to models ma and mb that are related and should be close. The difference is
that merging the data sets assumes that the signal-to-noise ratios are about
the same in the two experiments. Merging the models allows one model to
much more uncertain, e.g, due to more disturbances in that experiment. If the
conditions are about the same, it is recommended to merge data rather than
models, since this is more efficient and typically involves better conditioned
calculations.
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midprefs
Purpose
Select a directory for idprefs.mat, a file that stores the graphical user
interface’s start-up information.
Syntax
midprefs
midprefs(path)
Description
The graphical user interface ident allows a large number of variables for
customized choices. These include the window layout, the default choices of
plot options, and names and directories of the four most recent sessions with
ident. This information is stored in the file idprefs.mat, which should be
placed on the user’s MATLABPATH. The default, automatic location for this file is
in the same directory as the user’s startup.m file.
4midprefs
midprefs is used to select or change the directory where you store
idprefs.mat. Either type midprefs, and follow the instructions, or give the
directory name as the argument. Include all directory delimiters as in the PC
case
midprefs('c:\matlab\toolbox\local\')
or in the UNIX case
midprefs('/home/ljung/matlab/')
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misdata
4misdata
Purpose
Reconstruct missing input and output data.
Syntax
Datae = misdata(Data)
Datae = misdata(Data,Model)
Datae = misdata(Data,Maxiter,Tol)
Description
Data is input-output data in the iddata object format. Missing data samples
(both in inputs and in outputs) are entered as NaN.
Datae is an iddata object where the missing data has been replaced by
reasonable estimates.
Model is any idmodel (idarx, idgrey, idpoly, idss) used for the
reconstruction of missing data.
If no suitable model is known, it will be estimated in an iterative fashion using
default order state-space models.
Maxiter is the maximum number of iterations carried out (default 10). The
iterations will be terminated when the difference between two consecutive data
estimates differ by less than tol%. The default value of tol is 1.
Algorithm
For a given model, the missing data are estimated as parameters so as to
minimize the output prediction errors obtained from the reconstructed data.
See Section 14.2 in Ljung (1999). Treating missing outputs as parameters is
not the best approach from a statistical point of view, but is a good
approximation in many cases.
When no model is given, the algorithm alternates between estimating missing
data and estimating models, based on the current reconstruction.
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nkshift
4nkshift
Purpose
Shift data sequences.
Syntax
Datas = nkshift(Data,nk)
Description
Data contains input-output data in the iddata format.
nk is a row vector with the same length as the number of input channels in
Data.
Datas is an iddata object where the input channels in Data have been shifted
according to nk. A positive value of nk(ku) means that input channel number
ku is delayed nk(ku) samples.
nkshift lives in symbiosis with the InputDelay property of idmodel
m1 = pem(dat,4,'InputDelay',nk)
is related to
m2 = pem(nkshift(dat,nk),4);
such that m1 and m2 are the same models, but m1 stores the delay information
for use when frequency responses etc. are computed.
Note the difference with the idss and idpoly property nk
m3 = pem(dat,4,'nk',nk)
gives a model which itself explicitly contains a delay of nk samples.
See Also
idss, Algorithm Properties
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noisecnv
4noisecnv
Purpose
Convert an idmodel with noise channels to a model with only measured
channels.
Syntax
mod1 = noisecnv(mod)
mod2 = noisecnv(mod,'norm')
Description
mod is any idmodel, idarx, idgrey, idpoly or idss.
The noise input channels in mod are converted as follows: Consider a model
with both measured input channels u (nu channels) and noise channels e (ny
channels) with covariance matrix Λ
y = Gu + He
cov ( e ) = Λ = LL′
where L is a lower triangular matrix. Note that mod.NoiseVariance = Λ . The
model can also be described with unit variance, normalized noise source v:
y = Gu + HLv
cov ( v ) = I
• mod1 = noisecnv(mod) converts the model to a representation of the system
[G H] with nu+ny inputs and ny outputs. All input are treated as measured,
and mod1 does not have any noise model. The former noise input channels
have names [email protected], where yname is the name of the corresponding output.
• mod2 = noisecnv(mod,'norm') converts the model to a representation of the
system [G HL] with nu+ny inputs and ny outputs. All input are treated as
measured, and mod2 does not have any noise model. The former noise input
channels have names [email protected], where yname is the name of the
corresponding output. Note that the noise variance matrix factor L typically
is uncertain (has a non-zero covariance). This is taken into account in the
uncertainty description of mod2.
• If mod is a time series, that is nu = 0, mod1 is a model that describes the
transfer function H with measured input channels. Analogously, mod2
describes the transfer function HL.
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noisecnv
Note the difference with subreferencing:
• mod('m') gives a description of G only.
• mod('n') gives description of the noise model characteristics as a time series
model, i.e it describes H and also the covariance of e. In contrast,
noisecnv(m('n')) describes just the transfer function H. To obtain a
description of the normalized transfer function HL, use
noisecnv(m('n'),'norm')
Converting the noise channels to measured inputs is useful to study the
properties of the individual transfer functions from noise to output. It is also
useful for transforming idmodel objects to representations that do not handle
disturbance descriptions explicitly.
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nuderst
Purpose
4nuderst
Select the step-size for numerical differentiation.
Syntax
nds = nuderst(pars)
Description
The function pem uses numerical differentiation with respect to the model
parameters when applied to state-space structures. The same is true for many
functions that transform model uncertainties to other representations.
The step-size used in these numerical derivatives is determined by the M-file
nuderst. The output argument nds is a row vector whose k-th entry gives the
increment to be used when differentiating with respect the k-th element of the
parameter vector pars.
The default version of nuderst uses a very simple method. The step-size is the
maximum of 10 –4 times the absolute value of the current parameter and 10 – 7 .
You can adjust this to the actual value of the corresponding parameter by
editing nuderst. Note that the nominal value, for example 0, of a parameter
may not reflect its normal size.
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nyquist
Purpose
4nyquist
Plot Nyquist curve of frequency function.
Syntax
nyquist(m)
[fr,w] = nyquist(m)
[fr,w,covfr] = nyquist(m)
nyquist(m1,m2,m3,...,w)
nyquist(m1,'PlotStyle1',m2,'PlotStyle2',...)
nyquist(m1,m2,m3,..'sd*5',sd,'mode',mode)
Description
nyquist computes the complex-valued frequency response of idmodel and
idfrd models. When invoked without left-hand arguments, nyquist produces
a Nyquist plot on the screen, that is, a graph of the frequency response's
imaginary part against its real part.
The argument m is an arbitrary idmodel or idfrd model. This model can be
continuous or discrete, and SISO or MIMO. The InputNames and OuputNames
of the models are used to plot the responses for different I/O channels in
separate plots. Pressing the Enter key advances the plot from one input-output
pair to the next one. Specific I/O channels can be selected by the normal
subreferencing: m(ky,ku). With mode = 'same' all plots are given in the same
diagram.
nyquist(m,w) explicitly specifies the frequency range or frequency points to be
used for the plot. To focus on a particular frequency interval [wmin,wmax], set
w = {wmin,wmax}. (Notice the curly brackets.) To use particular frequency
points, set w to the vector of desired frequencies. Use logspace to generate
logarithmically spaced frequency vectors. All frequencies should be specified in
radians/sec.
nyquist(m1,m2,...,mN) or nyquist(m1,m2,...mN,w) plots the Bode
responses of several idmodels or idfrd models on a single figure. The models
may be mixes of different sizes and continuous/discrete. The sorting of the plots
is made based on the InputNames and OutputNames.
nyquist(m1,'PlotStyle1',...,mN,'PlotStyleN') further specifies which
color, linestyle, and/or marker should be used to plot each system, as in
nyquist(m1,'r--',m2,'gx')
When sd is specified as a number larger than zero, confidence regions will also
be plotted. These are ellipses in the complex plane and correspond to the region
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nyquist
where the true response at the frequency in question is to be found with a
confidence, corresponding to sd standard deviations (of the Gaussian
distribution).
If the argument indicating standard deviations is given as in ‘sd+5’, a
confidence region is plotted for every 5:th frequency, marking the center point
by ‘+’. The default is ‘sd+10’.
Note that the frequencies cannot be specified for idfrd objects. For those, the
plot and response are calculated for the internally stored frequencies. If the
frequencies w are specified when several models are treated, they will apply to
all non-idfrd models in the list. If you want different frequencies for different
models, you should thus first convert them to idfrd objects using the idfrd
command.
For time-series models (no input channels) the Nyquist plot is not defined.
Arguments
When nyquist is called with a single system and output arguments
fr = nyquist(m,w) or [fr,w,covfr] = nyquist(m)
no plot is drawn on the screen. If m has ny outputs and nu inputs, and w contains
nw frequencies, then fr is an ny-by-nu-by-Nw array such that fr(ky,ku,k) gives
the complex-valued frequency response from input ku to output ky at the
frequency w(k). For a SISO model, use fr(:) to obtain a vector of the frequency
response. The uncertainty information covfr is a 5-D array where
covfr(ky,ku,k,:,:)) is the 2-by-2 covariance matrix of the response from
input ku to output ky at frequency w(k). The 1,1 element is the variance of the
real part, the 2,2 element the variance of the imaginary part and the 1,2 and
2,1 elements the covariance between the real and imaginary parts.
squeeze(covfr(ky,ku,k,:,:)) gives the covariance matrix of the
corresponding response.
If m is a time series (no input), fr is returned as the (power) spectrum of the
outputs; an ny-by-ny-by-Nw array. Hence fr(:,:,k) is the spectrum matrix at
frequency w(k). The element fr(k1,k2,k) is the cross spectrum between
outputs k1 and k2 at frequency w(k). When k1=k2, this is the real-valued power
spectrum of output k1. covfr is then the covariance of the spectrum fr, so that
covfr(k1,k1,k) is the variance of the power spectrum of output k1 at
frequency w(k). No information about the variance of the cross spectra is
normally given. (That is, covfr(k1,k2,k) = 0 for k1 not equal to k2.)
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nyquist
If the model m is not a time series, use fr = nyquist(m('n')) to obtain the
spectrum information of the noise (output disturbance) signals.
Examples
See Also
g = spa(data)
m = n4sid(data,3)
nyquist(g,m,3)
bode, etfe, ffplot, idfrd, spa
4-117
n4sid
Purpose
4n4sid
Estimate state-space models using a subspace method.
Syntax
m = n4sid(data)
m = n4sid(data,order,'Property1',Value1,...,'PropertyN',ValueN)
Description
The function n4sid estimates models in state-space form, and returns them as
an idss object m. It handles an arbitrary number of input and outputs,
including the time-series case (no input). The state-space model is in the
innovations form
x ( t + Ts ) = Ax ( t ) + Bu ( t ) + Ke ( t )
y ( t ) = Cx ( t ) + Du ( t ) + e ( t )
m: The resulting model as an idss object.
data: An iddata object containing the output-input data.
order: The desired order of the state-space model. If order is entered as a row
vector (like order = [1:10]), preliminary calculations for all the indicated
orders are carried out. A plot will then be given that shows the relative
importance of the dimension of the state vector. More precisely, the singular
values of the Hankel matrices of the impulse response for different orders are
graphed. You will be prompted to select the order, based on this plot. The idea
is to choose an order such that the singular values for higher orders are
comparatively small. If order = 'best', a model of “best” (default choice) order
is computed, among the orders 1:10. This is the default choice of order.
The list of property name/property value pairs may contain any idss and
algorithm properties. See idss and Algorithm Properties.
idss properties that are of particular interest for n4sid are:
nk: The delays from the inputs to the outputs, a row vector with the same
number of entries as the number of input channels. Default is nk = [1 1 ...
1]. Note that delays being 0 or 1 show up as zeros or estimated parameters in
the D matrix. Delays larger than 1 means that a special structure of the A, B
and C matrices are used to accommodate the delays. This also means that the
actual order of the state-space model will be larger than order.
• CovarianceMatrix (can be abbreviated to 'co'): Setting CovarianceMatrix
to 'None' will block all calculations of uncertainty measures. These may
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n4sid
take the major part of the computation time. Note that, for a 'Free'
parameterization, the individual matrix elements cannot be associated with
any variance (these parameters are not identifiable). Instead, the resulting
model m stores a hidden state-space model in canonical form, that contains
covariance information. This is used when the uncertainty of various
input-output properties are calculated. It can also be retrieved by m.ss =
'can'. The actual covariance properties of n4sid estimates are not known
today. Instead the Cramer-Rao bound is computed and stored as an
indication of the uncertainty.
• DisturbanceModel: Setting DisturbanceModel to ‘None’ will deliver a model
with K = 0. This will have no direct effect on the dynamics model, other that
that the default choice of N4Horizon will not involve past outputs.
• InitialState: The initial state is always estimated for better accuracy.
However. it is returned with m only if InitialState = ‘Estimate’.
Algorithm properties that are special interest are:
• Focus: Assumes the values 'Prediction' (default), 'Simulation', ‘
Stability’, or any SISO linear filter (given as an LTI or idmodel object, or
as filter coefficients. See Algorithm Properties.) Setting ’Focus' to
’Simulation' chooses weights that should give a better simulation
performance for the model. In particular, a stable model is guaranteed.
Selecting a linear filter will focus the fit to the frequency ranges that are
emphasized by this filter.
• N4Weight: This property determines some weighting matrices used in the
singular-value decomposition that is a central step in the algorithm. Two
choices are offered: 'MOESP' that corresponds to the MOESP algorithm by
Verhaegen, and 'CVA' which is the canonical variable algorithm by
Larimore. The default value is 'N4Weight' = ’Auto', which gives an
automatic choice between the two options. m.EstimationInfo.N4Weight
tells you what the actual choice turned out to be.
• N4Horizon: Determines the prediction horizons forward and backward, used
by the algorithm. This is a row vector with three elements: N4Horizon =[r
sy su], where r is the maximum forward prediction horizon, i.e., the
algorithms uses up to r-step ahead predictors. sy is the number of past
outputs, and su is the number of past inputs that are used for the
predictions. See pages 209-210 in Ljung(1999) for the exact meaning of this.
These numbers may have a substantial influence of the quality of the
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n4sid
resulting model, and there are no simple rules for choosing them. Making
'N4Horizon' a k-by-3 matrix, means that each row of 'N4Horizon' will be
tried out, and the value that gives the best (prediction) fit to data will be
selected. (This option cannot be combined with selection of model order.) If
the property 'Trace' is 'On', information about the results will be given in
the MATLAB command window.
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n4sid
If you specify only one column in 'N4Horizon', the interpretation is r=sy=su.
The default choice is 'N4Horizon' = ’Auto', which uses an Akaike
Information Criterion (AIC) for the selection of sy and su. If
’DisturbanceModel' = 'None', sy is set to 0. Typing
m.EstimationInfor.N4Horizon will tell you what the final choice of horizons
were.
Algorithm
The variants of the implemented algorithm are described in Section 10.6 in
Ljung (1999).
Examples
Build a fifth order model from data with three inputs and two outputs. Try
several choices of auxiliary orders. Look at the frequency response of the model.
z = iddata([y1 y2],[ u1 u2 u3]);
m = n4sid(z,5,'n4h',[7:15]','trace','on');
bode(m,'sd',3)
Estimate a continuous-time model, in a canonical form parameterization,
focusing on the simulation behavior. Determine the order yourself after seeing
the plot of singular values.
m = n4sid(m,[1:10],'foc','sim','ssp','can','ts',0)
bode(m)
See Also
idss, pem, Algorithm Properties
References
P. vanOverschee and B. DeMoor: Subspace Identification of Linear Systems:
Theory, Implementation, Applications. Kluwer Academic Publishers, 1996.
M. Verhaegen: Identification of the deterministic part of MIMO state space
models. Automatica, Vol 30, pp 61-74, 1994.
W.E. Larimore: Canonical variate analysis in identification, filtering and
adaptive control. In Proc. 29th IEEE Conference on Decision and Control, pp
596-604, Honolulu, 1990.
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oe
Purpose
4oe
Estimate the parameters of an Output-Error model.
Syntax
m = oe(data,orders)
m = oe(data,'nb',nb,'nf',nf,'nk',nk)
m = oe(data,orders,'Property1',Value1,'Property2',Value2,...)
Description
oe returns m as an idpoly object with the resulting parameter estimates,
together with estimated covariances.The parameters of the Output-Error
model structure
B( q)
y ( t ) = ------------ u ( t – nk ) + e ( t )
F( q)
are estimated using a prediction error method.
data is an iddata object containing the output-input data. The structure
information can either be given explicitly as
(...,'nb',nb,'nf',nf,'nk',nk,...), or in the argument orders given as
orders = [nb nf nk]
The parameters nb and nf are the orders of the Output-Error model and nk is
the delay. Specifically,
nb:
B ( q ) = b1 + b2 q
nf:
F ( q ) = 1 + f1 q
–1
–1
+ … + b nb q
+ … + f nf q
– nb + 1
– nf
Alternatively, you can specify the vector as
orders = mi
where mi is an initial guess at the Output-Error model given in idpoly format.
See “Polynomial Representation of Transfer Functions” on page 3-11 in the
“Tutorial” chapter for more information.
For multi-input systems, nb, nf, and nk are row vectors with as many entries
as there are input channels. Entry number i then describes the orders and
delays associated with the i-th input.
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oe
The structure and the estimation algorithm are affected by any property name/
property value pairs that are set in the input argument list. Useful properties
are 'Focus', 'InitialState', 'InputDelay', 'SearchDirection',
'MaxIter', 'Tolerance', 'LimitError', 'FixedParameter', and 'Trace'.
See Algorithm Properties, idpoly, and idmodel for details of these properties
and their possible values.
oe does not support multi-output models. Use state-space model for this case
(see n4sid and pem).
Algorithm
oe uses essentially the same algorithm as armax with modifications to the
computation of prediction errors and gradients.
See Also
armax, bj, idpoly, pem
4-123
pe
Purpose
4pe
Compute the prediction errors associated with a model and a data set.
Syntax
e = pe(m,data)
[e,x0] = pe(m,data,init)
Description
data is the output-input data set, given as an iddata object, and m is any
idmodel object.
e is returned as an iddata object, so that e.OutputData contains the prediction
errors that result when model m is applied to the data:
–1
e ( t ) = H ( q ) [ y ( t ) – G ( q )u ( t ) ]
The argument init determines how to deal with the initial conditions:
• init ='estimate' means that the initial state is chosen so that the norm of
prediction error is minimized. This initial state is returned as x0.
• init = 'zero' sets the initial state to zero.
• init = 'model' used the model’s internally stored initial state.
• init = x0, where x0 is a column vector of appropriate dimension uses that
value as initial state.
If init is not specified, the model property m.InitialState is used, so that
'Estimate', 'Backcast' and 'Auto' set init = 'Estimate', while
m.InitialState = 'Zero' sets init = 'zero', and 'Fixed' and 'Model'
set init = 'model'.
The output argument x0 is the used value of the initial state. If data contains
several experiments, x0 will be a matrix, containing the initial states from each
experiment.
See Also
4-124
idmodel, resid
pem
Purpose
4pem
Estimate the parameters of general linear models.
Syntax
m
m
m
m
m
m
m
Description
pem is the basic estimation command in the toolbox and covers a variety of
situations.
=
=
=
=
=
=
=
pem(data)
pem(data,mi)
pem(data,mi,'Property1',Value1,...,'PropertyN',ValueN)
pem(data,orders)
pem(data,'nx',ssorder)
pem(data,'na',na,'nb',nb,'nc',nc,'nd',nd,'nf',nf,'nk',nk)
pem(data,orders,'Property1',Value1,...,'PropertyN',ValueN)
data is always an iddata object that contains the input/output data.
With Initial Model
mi is any idmodel object, idarx, idpoly, idss, or idgrey. It could be a result of
another estimation routine, or constructed and modified by the constructors
(idpoly, idss, idgrey) and set. The properties of mi can also be changed by any
property name/property value pairs in pem as indicated in the syntax.
m is then returned as the best fitting model in the model structure defined by
mi.The iterative search is initialized at the parameters of the initial/nominal
model mi. m will be of the same class as mi.
Black-Box State-Space Models
With m = pem(data,n), where n is a positive integer, or m = pem(data,'nx',n)
a state-space model of order n is estimated. The default situation is that it is
estimated in a 'Free' parameterization, that can be further modified by the
properties 'nk', 'DisturbanceModel', and 'InitialState' (see the reference
pages for idss and n4sid). The model is initialized by n4sid, and then further
adjusted by optimizing the prediction error fit.
You can choose between several different orders by
m = pem(data,'nx',[n1,n2,...nN])
and you will then be prompted for the “best” order. By
m = pem(data,'best')
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pem
an automatic choice of order among 1:10 is made.
m = pem(data)
is short for m = pem(data,'best'). To work with other delays use, e.g. m =
pem(data,'best','nk',[0,...0]).
In this case m is returned as an idss model.
Black-Box Multiple-Input-Single-Output Models
The function pem also handles the general multi-input-single-output structure
B1 ( q )
B nu ( q )
C( q)
A ( q )y ( t ) = --------------- u 1 ( t – nk 1 ) + … + ------------------- u nu ( t – nk nu ) + ------------- e ( t )
F1 ( q )
F nu ( q )
D(q)
The orders of this general model are given either as
orders = [na nb nc nd nf nk]
or with (...'na',na,'nb',nb,...) as shown in the syntax. Here na, nb, nc,
nd, and nf are the orders of the model and nk is the delay(s). For multi-input
systems, nb, nf, and nk are row vectors giving the orders and delays of each
input. (See “Polynomial Representation of Transfer Functions” on page 3-11 in
the “Tutorial” chapter for exact definitions of the orders.) When the orders are
specified with separate entries, those not given are taken as zero.
In this case m is returned as an idpoly object.
Typical
Properties to
set
In all cases the algorithm is affected by the properties (see Algorithm
Properties for details):
• Focus, with possible values 'Prediction' (Default), 'Simulation' or a SISO
filter (given as an LTI or idmodel object or as filter coefficients)
• MaxIter and Tolerance govern the stopping criteria for the iterative search.
• LimitError deals with how the criterion can be made less sensitive to
outliers and bad data
• MaxSize determines the largest matrix ever formed by the algorithm. The
algorithm goes into for-loops to avoid larger matrices, which may be more
efficient than using virtual memory.
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pem
• Trace, with possible values 'Off', 'On', 'Full', that governs the
information sent to the MATLAB command window.
For black-box state-space models, also 'N4Weight' and 'N4Horizon' will affect
the result, since these models are initialized with n4sid estimate. See the
reference page for n4sid.
Typical idmodel properties to affect are (see idmodel properties for more
details):
• Ts, the sampling interval. Set 'Ts'=0 to obtain a continuous-time state-space
model. For discrete-time models, 'Ts' is automatically set to sampling
interval of the data. Note that, in the black box case, it is usually better to
first estimate a discrete-time model, and then convert that to continuous
time by d2c.
• nk, the time delays from the inputs (not applicable to structured state-space
models). Time delays specified by ’nk', will be included in the model.
• DisturbanceModes determines the parameterization of K for free and
canonical state-space parameterizations, as well as for idgrey models.
• InitialState. The initial state may have a substantial influence on the
estimation result for system with slow responses. It is most pronounced for
Output-Error models (K=0 for state-space, and na=nc=nd=0 for input/output
models). The default value 'Auto', estimates the influence of the initial state
and sets the value to 'Estimate', 'Backcast' or 'Zero', based on this
effect. Possible values of 'InitialState' are 'Auto', 'Estimate',
'Backcast', 'Zero' and ’Fixed'. See “Initial State” on page 3-91 in the
“Tutorial” chapter.
Examples
Here is an example of a system with three inputs and two outputs. A canonical
form state-space model of order 5 is sought.
z = iddata([y1 y2],[ u1 u2 u3]);
m = pem(z,5,'ss','can')
Building an ARMAX model for the response to output 2.
ma = pem(z(:,2,:),'na',2,'nb',[2 3 1],'nc',2,'nk',[1 2 0])
4-127
pem
Comparing the models (compare automatically matches the channels using the
channel names).
compare(z,m,ma)
Algorithm
pem uses essentially the same algorithm as armax with modifications to the
computation of prediction errors and gradients.
See Also
armax, bj, oe, idss, idpoly, idgrey, idmodel, Algorithm Properties,
EstimationInfo
4-128
plot (iddata)
Purpose
4plot (iddata)
Plot input-output iddata.
Syntax
plot(data)
plot(d1,...,dN)
plot(d1,PlotStyle1,...,dN,PlotStyleN)
Description
data is the output-input data to be graphed, given as an iddata object. A split
plot is obtained with the outputs on top and the inputs at the bottom.
One plot for each I/O channel combination is produced. Pressing the Return
key advances the plot.
To plot a specific interval, use plot(data(200:300)). To plot specific input/
output channels, use plot(data(:,ky,ku)), consistent with the
subreferencing of iddata objects. (See iddata).
If data.intersample = 'zoh', the input is piecewise constant between
sampling points, and it is then graphed accordingly.
To plot several iddata sets d1,...,dN, use plot(d1,...,dN). I/O channels
with the same experiment name, input name, and output name, will always be
plotted in the same plot.
With PlotStyle, the color, linestyle, and marker of each data set can be
specified
plot(d1,'y:*',d2,'b')
just as in the regular plot command.
See Also
iddata
4-129
plot (idmodepolydata)
Purpose
4plot (idmodepolydata)
Convert a model to input-output polynomials.
Syntax
[A,B,C,D,F] = polydata(m)
[A,B,C,D,F,dA,dB,dC,dD,dF] = polydata(m)
Description
This is essentially the inverse of the idpoly constructor. It returns the
polynomials of the general model
B1 ( q )
B nu ( q )
C( q)
A ( q )y ( t ) = --------------- u 1 ( t – nk 1 ) + … + ------------------- u nu ( t – nk nu ) + ------------- e ( t )
D( q )
F1 ( q )
F nu ( q )
as contained in the model m.
dA, dB etc. are the standard deviations of A, B, etc.
m can be any single output idmodel, i.e., not just idpoly. For multi-output
models you can use [A,B,C,D,F] = polydata(m(ky,:)) to obtain the
polynomials for the ky-th output.
See Also
4-130
idmodel, idpoly, tfdata
predict
Purpose
4predict
Predict the output k steps ahead.
Syntax
yp = predict(m,data)
[yp,mpred] = predict(m,data,k,init)
Description
data is the output-input data as an iddata object, and m is any idmodel object
(idpoly, idss, idgrey, or idarx)
The argument k indicates that the k step ahead prediction of y according to the
model m is computed. In the calculation of yp(t) the model can use outputs up
to time
t – k :y ( s ) ,s = t – k ,t – k – 1 ,…
and inputs up to the current time t. The default value of k is 1.
The output yp is an iddata object containing the predicted values as
OutputData. The output argument mpred contains the k-step ahead predictor.
This is given as a cell array, whose k:th entry is an idpoly model for the
predictor of output number k.
init determines how to deal with the initial state:
• init ='estimate': The initial state is set to value that minimizes the norm
of the prediction error associated with the model and the data.
• init = 'zero' sets the initial state to zero.
• init = 'model' used the model's internally stored initial state
• init = x0, where x0 is a column vector of appropriate dimension uses that
value as initial state
If init is not specified, the model property m.InitialState is used, so that
'Estimate', 'Backcast' and 'Auto' set init = 'Estimate', while
m.InitialState = 'Zero' sets init = 'zero', and 'Model' and 'Fixed' set
init = 'model'.
An important use of predict is to evaluate a model’s properties in the
mid-frequency range. Simulation with sim (which conceptually corresponds to
k = inf) can lead to levels that drift apart, since the low frequency behavior is
emphasized. One step ahead prediction is not a powerful test of the model’s
properties, since the high frequency behavior is stressed. The trivial predictor
4-131
predict
ŷ ( t ) = y ( t – 1 ) can give good predictions in case the sampling of the data is
fast.
Another important use of predict is to evaluate models of time series. The
natural way of studying a time-series model’s ability to reproduce
observations is to compare its k-step ahead predictions with actual data.
Note that for Output-Error models, there is no difference between the k-step
ahead predictions and the simulated output, since, by definition, Output-Error
models only use past inputs to predict future outputs.
Algorithm
The model is evaluated in state-space form, and the state equations are
simulated k-steps ahead with initial value x ( t – k ) = x̂ ( t – k ) , where x̂ ( t – k )
is the Kalman filter state estimate.
Examples
Simulate a time series, estimate a model based on the first half of the data, and
evaluate the four step ahead predictions on the second half.
m0 = idpoly([1 -0.99],[],[1 -1 0.2]);
e = iddata([],randn(400,1));
y = sim(m0,e);
m = armax(y(1:200),[1 2]);
yp = predict(m,y,4);
plot(y(201:400),yp(201:400))
Note that the last two commands also are achieved by
compare(y,m,4,201:400);
See Also
4-132
compare, sim, pe
present
Purpose
4present
Display the information in an idmodel model, including uncertainty.
Syntax
present(m)
Description
This function displays the model m, together with the estimated standard
deviations of the parameters, loss function, and Akaike’s Final Prediction
Error (FPE) Criterion (which essentially equals the AIC). It also displays
information about how m was created.
present thus gives more detailed information about the model than the
standard display function.
4-133
pzmap
Purpose
4pzmap
Plot zeros and poles.
Syntax
pzmap(m)
pzmap(m,'sd',sd)
pzmap(m1,m2,m3,...)
pzmap(m1,'PlotStyle1',m2,'PlotStyle2',...,'sd',sd)
pzmap(m1,m2,m3,..,'sd',sd,mode,axis)
Description
m is any idmodel object: idarx, idgrey, idss, or idpoly.
The zeros and poles of m are graphed, with o denoting zeros and x denoting
poles. Poles and zeros at infinity are ignored. For discrete-time models, zeros
and poles at the origin are also ignored.
If sd has a value larger than zero, confidence regions around the poles and
zeros are also graphed. The regions corresponding to sd standard deviations
are marked. The default value is sd = 0. Note that the confidence regions may
sometimes stretch outside the plot, but they are always symmetric around the
indicated zero or pole.
If the poles and zeros are associated with a discrete-time model, a unit circle is
also drawn. For continuous-time models the real and imaginary axes are
drawn
When mi contain information about several different input/output channels,
there are some options:
mode = 'sub' splits the screen into several plots, one for each input/output
channel. These are based on the InputName and OutputName properties
associated with the different models.
mode = 'same' gives all plots in the same diagram. Pressing the Return key
advances the plots.
mode = 'sep' erases the previous plot before the next channel pair is treated.
The default value is mode = 'sub'.
axis = [x1 x2 y1 y2] fixes the axis scaling accordingly. axis = s is the same
as
axis = [-s s -s s]
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pzmap
The colors associated with the different models can be selected by the
arguments PlotStyle. Use PlotStyle = ’b', ’g', etc. Markers and line styles
are not used.
The noise input channels in m are treated as follows: Consider a model m with
both measured input channels u (nu channels) and noise channels e (ny
channels) with covariance matrix Λ
y = Gu + He
cov ( e ) = Λ = LL′
where L is a lower triangular matrix. Note that m.NoiseVariance = Λ . The
model can also be described with unit variance, normalized noise source v
y = Gu + HLv
cov ( v ) = I
Then:
• pzmap(m) plots the zeros and poles of the transfer function G.
• pzmap(m('n')) plots the zeros and poles of the transfer function H. (ny
inputs and ny outputs). The input channels have names [email protected], where
yname is the name of the corresponding output.
• If m is a time series, that is nu = 0, pzmap(m) plots the zeros and poles of the
the transfer function H.
• pzmap(noisecnv(m)) plots the zeros and poles of the transfer function [G H]
(nu+ny inputs and ny outputs). The noise input channels have names
[email protected], where yname is the name of the corresponding output.
• pzmap(noisecnv(m,'norm')) plots the zeros and poles of the transfer
function [G HL] (nu+ny inputs and ny outputs. The noise input channels
have names [email protected], where yname is the name of the corresponding output.
Examples
mbj = bj(data,[2 2 1 1 1]);
mar = armax(data,[2 2 2 1]);
pzmap(mbj,mar,'sd',3)
shows all zeros and poles of two models along with the confidence regions
corresponding to three standard deviations.
See Also
idmodel, zpkdata
4-135
rarmax
Purpose
4rarmax
Estimate recursively the parameters of an ARMAX or ARMA model.
Syntax
thm = rarmax(z,nn,adm,adg)
[thm,yhat,P,phi,psi] = rarmax(z,nn,adm,adg,th0,P0,phi0,psi0)
Description
The parameters of the ARMAX model structure
A ( q )y ( t ) = B ( q )u ( t – nk ) + C ( q )e ( t )
are estimated using a recursive prediction error method.
The input-output data are contained in z, which is either an iddata object or a
matrix z = [y u] where y and u are column vectors. nn is given as
nn = [na nb nc nk]
where na, nb, and nc are the orders of the ARMAX model, and nk is the delay.
Specifically,
–1
na:
A ( q ) = 1 + a1 q
nb:
B( q) = b1 + b2 q
nc:
C ( q ) = 1 + c1 q
+ … + a na q
–1
–1
– na
+ … + b nb q
+ … + c nc q
– nb + 1
– nc
See “Polynomial Representation of Transfer Functions” on page 3-11 in the
“Tutorial” chapter for more information.
If z represents a time series y and nn = [na nc], rarmax estimates the
parameters of an ARMA model for y.
A ( q )y ( t ) = C ( q )e ( t )
Only single-input, single-output models are handled by rarmax. Use rpem for
the multi-input case.
The estimated parameters are returned in the matrix thm. The k-th row of thm
contains the parameters associated with time k, i.e., they are based on the data
in the rows up to and including row k in z. Each row of thm contains the
estimated parameters in the following order.
4-136
rarmax
thm(k,:) = [a1,a2,...,ana,b1,...,bnb,c1,...,cnc]
yhat is the predicted value of the output, according to the current model, i.e.,
row k of yhat contains the predicted value of y(k) based on all past data.
The actual algorithm is selected with the two arguments adm and adg. These
are described under rarx.
The input argument th0 contains the initial value of the parameters, a row
vector, consistent with the rows of thm. The default value of th0 is all zeros.
The arguments P0 and P are the initial and final values, respectively, of the
scaled covariance matrix of the parameters. See rarx. The default value of P0
is 104 times the unit matrix. The arguments phi0, psi0, phi, and psi contain
initial and final values of the data vector and the gradient vector, respectively.
The sizes of these depend in a rather complicated way on the chosen model
orders. The normal choice of phi0 and psi0 is to use the outputs from a
previous call to rarmax with the same model orders. (This call could of course
be a dummy call with default input arguments.) The default values of phi0 and
psi0 are all zeros.
Note that the function requires that the delay nk be larger than 0. If you want
nk=0, shift the input sequence appropriately and use nk=1.
Algorithm
The general recursive prediction error algorithm (11.44), (11.47)-(11.49) of
Ljung (1999) is implemented. See “Recursive Parameter Estimation” on
page 3-78 in the “Tutorial” chapter for more information.
Examples
Compute and plot, as functions of time, the four parameters in a second order
ARMA model of a time series given in the vector y. The forgetting factor
algorithm with a forgetting factor of 0.98 is applied.
thm = rarmax(y,[2 2],'ff',0.98);
plot(thm)
4-137
rarx
Purpose
4rarx
Estimate recursively the parameters of an ARX or AR model.
Syntax
thm = rarx(z,nn,adm,adg)
[thm,yhat,P,phi] = rarx(z,nn,adm,adg,th0,P0,phi0)
Description
The parameters of the ARX model structure
A ( q )y ( t ) = B ( q )u ( t – nk ) + e ( t )
are estimated using different variants of the recursive least-squares method.
The input-output data are contained in z, which is either an iddata object or a
matrix z = [y u] where y and u are column vectors. nn is given as
nn = [na nb nk]
where na and nb are the orders of the ARX model, and nk is the delay.
Specifically,
–1
na:
A ( q ) = 1 + a1 q
nb:
B ( q ) = b1 + b2 q
+ … + a na q
–1
– na
+ … + b nb q
– nb + 1
See equation (Equation 3-13) in the “Tutorial” chapter for more information.
If z is a time series y and nn = na, rarx estimates the parameters of an AR
model for y.
A ( q )y ( t ) = e ( t )
Models with several inputs
A ( q )y ( t ) = B 1 ( q )u 1 ( t – nk 1 ) + …B nu u nu ( t – nk nu ) + e ( t )
are handled by allowing u to contain each input as a column vector,
u = [u1 ...
unu]
and by allowing nb and nk to be row vectors defining the orders and delays
associated with each input.
Only single-output models are handled by rarx.
4-138
rarx
The estimated parameters are returned in the matrix thm. The k-th row of thm
contains the parameters associated with time k, i.e., they are based on the data
in the rows up to and including row k in z. Each row of thm contains the
estimated parameters in the following order.
thm(k,:) = [a1,a2,...,ana,b1,...,bnb]
In the case of a multi-input model, all the b parameters associated with input
number 1 are given first, and then all the b parameters associated with input
number 2, and so on.
yhat is the predicted value of the output, according to the current model, i.e.,
row k of yhat contains the predicted value of y(k) based on all past data.
The actual algorithm is selected with the two arguments adg and adm. These
are described in “Recursive Parameter Estimation” on page 3-78 in the
“Tutorial” chapter. The options are as follows.
With adm ='ff' and adg = lam the forgetting factor algorithm
(Equation 3-60abd)+(Equation 3-62) is obtained with forgetting factor λ = lam.
This is what is often referred to as Recursive Least Squares, RLS. In this case
the matrix P (see below) has the following interpretation: R 2 /2 ∗ P is
approximately equal to the covariance matrix of the estimated parameters.
Here R 2 is the variance of the innovations (the true prediction errors e(t) in
(Equation 3-57)
With adm ='ug' and adg = gam, the unnormalized gradient algorithm
(Equation 3-60abc) + (Equation 3-63) is obtained with gain gamma= gam. This
algorithm is commonly known as normalized Least Mean Squares, LMS.
Similarly, adm ='ng' and adg = gam give the normalized gradient or
Normalized Least Mean Squares, NLMS algorithm (Equation 3-60abc) +
(Equation 3-64). In these cases, P is not defined or applicable.
With adm ='kf' and adg = R1 the Kalman Filter Based algorithm
(Equation 3-60) is obtained with R2= 1 and R1 = R1. If the variance of the
innovations e(t) is not unity but R 2 ; then R 2 ∗ P is the covariance matrix of the
parameter estimates, while R 1 =R1 / R 2 is the covariance matrix of the
parameter changes in (Equation 3-58).
4-139
rarx
The input argument th0 contains the initial value of the parameters; a row
vector, consistent with the rows of thm. (See above.) The default value of th0 is
all zeros.
The arguments P0 and P are the initial and final values, respectively, of the
scaled covariance matrix of the parameters. The default value of P0 is 104
times the identity matrix.
The arguments phi0 and phi contain initial and final values, respectively, of
the data vector.
ϕ ( t ) = [ y ( t – 1 ), …, y ( t – na ), u ( t – 1 ), …u ( t – nb – nk + 1 ) ]
Then, if
z = [y(1),u(1); ...
;y(N),u(N)]
you have phi0 = ϕ ( 1 ) and phi = ϕ ( N ) . The default value of phi0 is all zeros.
For online use of rarx, use phi0, th0, and P0 as the previous outputs phi, thm
(last row), and P.
Note that the function requires that the delay nk be larger than 0. If you want
nk=0, shift the input sequence appropriately and use nk=1. See nkshift.
Examples
Adaptive noise canceling: The signal y contains a component that has its origin
in a known signal r. Remove this component by estimating, recursively, the
system that relates r to y using a sixth order FIR model together with the
NLMS algorithm.
z = [y r];
[thm,noise] = rarx(z,[0 6 1],'ng',0.1);
% noise is the adaptive estimate of the noise
% component of y
plot(y-noise)
If the above application is a true online one, so that you want to plot the best
estimate of the signal y - noise at the same time as the data y and u become
available, proceed as follows.
phi = zeros(6,1); P=1000∗eye(6);
th = zeros(1,6); axis([0 100 -2 2]);
plot(0,0,'∗'), hold on
% The loop:
4-140
rarx
while ~abort
[y,r,abort] = readAD(time);
[th,ns,P,phi] = rarx([y r],'ff',0.98,th,P,phi);
plot(time,y-ns,'∗')
time = time +Dt
end
This example uses a forgetting factor algorithm with a forgetting factor of 0.98.
readAD represents an M-file that reads the value of an A/D converter at the
indicated time instant.
4-141
rbj
Purpose
4rbj
Estimate recursively the parameters of a Box-Jenkins model.
Syntax
thm = rbj(z,nn,adm,adg)
[thm,yhat,P,phi,psi] = ... rbj(z,nn,adm,adg,th0,P0,phi0,psi0)
Description
The parameters of the Box-Jenkins model structure
B(q)
C (q )
y ( t ) = ------------ u ( t – nk ) + ------------- e ( t )
F(q)
D(q)
are estimated using a recursive prediction error method.
The input-output data are contained in z, which is either an iddata object or a
matrix z = [y u] where y and u are column vectors. nn is given as
nn = [nb nc nd nf nk]
where nb, nc, nd, and nf are the orders of the Box-Jenkins model, and nk is the
delay. Specifically,
nb:
B ( q) = b1 + b2 q
nc:
C ( q ) = 1 + c1 q
nd:
D ( q ) = 1 + d1 q
nf:
F ( q ) = 1 + f1 q
–1
–1
+ … + c nc q
–1
–1
+ … + b nb q
– nc
+ … + d nd q
+ … + f nf q
– nb + 1
– nd
– nf
See “Polynomial Representation of Transfer Functions” on page 3-11 in the
“Tutorial” chapter for more information.
Only single-input, single-output models are handled by rbj. Use rpem for the
multi-input case.
The estimated parameters are returned in the matrix thm. The k-th row of thm
contains the parameters associated with time k, i.e., they are based on the data
in the rows up to and including row k in z. Each row of thm contains the
estimated parameters in the following order.
thm(k,:) = [b1,...,bnb,c1,...,cnc,d1,...,dnd,f1,...,fnf]
4-142
rbj
yhat is the predicted value of the output, according to the current model, i.e.,
row k of yhat contains the predicted value of y(k) based on all past data.
The actual algorithm is selected with the two arguments adm and adg. These
are described under rarx.
The input argument th0 contains the initial value of the parameters, a row
vector, consistent with the rows of thm. (See above.) The default value of th0 is
all zeros.
The arguments P0 and P are the initial and final values, respectively of the
scaled covariance matrix of the parameters. See rarx. The default value of P0
is 104 times the unit matrix. The arguments phi0, psi0, phi, and psi contain
initial and final values of the data vector and the gradient vector, respectively.
The sizes of these depend in a rather complicated way on the chosen model
orders. The normal choice of phi0 and psi0 is to use the outputs from a
previous call to rbj with the same model orders. (This call could, of course, be
a dummy call with default input arguments.) The default values of phi0 and
psi0 are all zeros.
Note that the function requires that the delay nk is larger than 0. If you want
nk=0, shift the input sequence appropriately and use nk=1.
Algorithm
The general recursive prediction error algorithm (11.44) of Ljung (1900) is
implemented. See also “Recursive Parameter Estimation” on page 3-78 in the
“Tutorial” chapter.
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resample
Purpose
4resample
Resample data by interpolation and decimation.
Syntax
datar = resample(data,P,Q)
datar = resample(data,P,Q,,filter_order)
Description
data: The data to be resampled, given as an iddata object
datar: The resampled data returned as an iddata object
P,Q: Integers that determine the resampling factor. The new sampling interval
will be Q/P times the original one, so resample(z,1,Q) means decimation with
a factor Q.
filter_order: Determines the order of the presampling filters used before
interpolation and decimation. Default is 10.
Algorithm
If the Signal Processing Toolbox is available, the resampling is achieved by
calls to the resample function in that toolbox. The intersample character of the
input, as described by data.InterSample, is taken into account.
Otherwise, the data are interpolated by a factor P and then decimated by a
factor Q. The interpolation and decimation are preceded by prefiltering, and
follow the same algorithms as in the routines interp and decimate in the
Signal Processing Toolbox.
Example
Resample by a increasing the sampling rate a factor1.5 and compare the
signals.
plot(u)
ur = resample(u,3,2);
plot(u,ur)
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resid
Purpose
4resid
Compute and test the residuals (prediction errors) of a model.
Syntax
resid(m,data)
resid(m,data,Type)
resid(m,data,Type,M)
e = resid(m,data);
Description
data contains the output-input data as an iddata object
m is the model to be evaluated on the given data set. It is any idmodel object.
In all cases the residuals e associated with the data and the model are
computed. This is done as in the command pe with a default choice of init.
When called without output arguments, resid produces a plot. The plot can be
of three kinds depending on the argument Type:
• Type = 'Corr' (default): The autocorrelation function of e and the cross
correlation between e and the input(s) u are computed and displayed. The
99% confidence intervals for these values are also computed and shown as a
yellow region. The computation of the confidence region is done assuming e
to be white and independent of u. The functions are displayed up to lag M,
which is 25 by default.
• Type = 'ir': The impulse response (up to lag M, which is 25 by default) from
the input to the residuals is plotted with a 99% confidence region around zero
marked as a yellow area. Negative lags up tp M/4 are also included to
investigate feedback effects. (The result is the same as
impulse(e,'sd',2.58,'fill',M).)
• Type = 'fr': The frequency response from the input to the residuals (based
on a high order FIR model) is shown as a Bode plot. A 99% confidence region
around zero is also marked as a yellow area.
With an output argument, no plot is produced, and e is returned with the
residuals (prediction errors) associated with the model and the data. It is an
iddata object with the residuals as outputs and the input in data as inputs.
That means that e can be directly used to build model error models, i.e., models
that describe the dynamics from the input to the residuals (which should be
negligible if m is a good description of the system).
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resid
See “Model Structure Selection and Validation” on page 3-63 in the “Tutorial”
chapter for more information.
Examples
Here are some typical model validation commands.
e = resid(m,data);
plot(e)
compare(data,m);
To compute a “model error model,” that is, a model to input to the residuals to
see if any essential unmodeled dynamics are left,
e = resid(m,data);
me = arx(e,[10 10 0]);
bode(me,'sd',3,fill')
See Also
compare, idgrey, idarx, idpoly, idss, pem
References
Ljung (1999), Section 16.6.
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roe
Purpose
4roe
Estimate recursively the parameters of an Output-Error model.
Syntax
thm = roe(z,nn,adm,adg)
[thm,yhat,P,phi,psi] = roe(z,nn,adm,adg,th0,P0,phi0,psi0)
Description
The parameters of the Output-Error model structure
B( q)
y ( t ) = ------------ u ( t – nk ) + e ( t )
F(q)
are estimated using a recursive prediction error method.
The input-output data are contained in z, which is either an iddata object or a
matrix z = [y u] where y and u are column vectors. nn is given as
nn = [nb nf nk]
where nb and nf are the orders of the Output-Error model, and nk is the delay.
Specifically,
nb:
B ( q ) = b 1 + b2 q
nf:
F ( q ) = 1 + f1 q
–1
–1
+ … + b nb q
+ … + f nf q
– nb + 1
– nf
See “Polynomial Representation of Transfer Functions” on page 3-11 in the
“Tutorial” chapter for more information.
Only single-input, single-output models are handled by roe. Use rpem for the
multi-input case.
The estimated parameters are returned in the matrix thm. The k-th row of thm
contains the parameters associated with time k, i.e., they are based on the data
in the rows up to and including row k in z.
Each row of thm contains the estimated parameters in the following order.
thm(k,:) = [b1,...,bnb,f1,...,fnf]
yhat is the predicted value of the output, according to the current model, i.e.,
row k of yhat contains the predicted value of y(k) based on all past data.
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roe
The actual algorithm is selected with the two arguments adg and adm. These
are described under rarx.
The input argument th0 contains the initial value of the parameters, a row
vector, consistent with the rows of thm. (See above.) The default value of th0 is
all zeros.
The arguments P0 and P are the initial and final values, respectively, of the
scaled covariance matrix of the parameters. See rarx. The default value of P0
is 104 times the unit matrix. The arguments phi0, psi0, phi, and psi contain
initial and final values of the data vector and the gradient vector, respectively.
The sizes of these depend in a rather complicated way on the chosen model
orders. The normal choice of phi0 and psi0 is to use the outputs from a
previous call to roe with the same model orders. (This call could be a dummy
call with default input arguments.) The default values of phi0 and psi0 are all
zeros.
Note that the function requires that the delay nk is larger than 0. If you want
nk=0, shift the input sequence appropriately and use nk=1.
Algorithm
The general recursive prediction error algorithm (11.44) of Ljung (1999) is
implemented. See also “Recursive Parameter Estimation” on page 3-78 in the
“Tutorial” chapter.
See Also
oe, rarx, rbj, rplr, rpem, nkshift
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rpem
Purpose
4rpem
Estimate recursively the parameters of a general multi-input single-output
linear model.
Syntax
thm = rpem(z,nn,adm,adg)
[thm,yhat,P,phi,psi] = rpem(z,nn,adm,adg,th0,P0,phi0,psi0)
Description
The parameters of the general linear model structure
B1 ( q )
B nu ( q )
C( q)
A ( q )y ( t ) = --------------- u 1 ( t – nk 1 ) + … + ------------------- u nu ( t – nk nu ) + ------------- e ( t )
D(q)
F1 ( q )
F nu ( q )
are estimated using a recursive prediction error method.
The input-output data are contained in z, which is either an iddata object or a
matrix z = [y u] where y and u are column vectors. (In the multi-input case u
contains one column for each input). nn is given as
nn = [na nb nc nd nf nk]
where na, nb, nc, nd, and nf are the orders of the model, and nk is the delay.
For multi-input systems nb, nf, and nk are row vectors giving the orders and
delays of each input. See “Polynomial Representation of Transfer Functions” on
page 3-11 in the “Tutorial” chapter for an exact definition of the orders.
The estimated parameters are returned in the matrix thm. The k-th row of thm
contains the parameters associated with time k, i.e., they are based on the data
in the rows up to and including row k in z. Each row of thm contains the
estimated parameters in the following order.
thm(k,:) = [a1,a2,...,ana,b1,...,bnb,...
c1,...,cnc,d1,...,dnd,f1,...,fnf]
For multi-input systems the B part in the above expression is repeated for each
input before the C part begins, and also the F part is repeated for each input.
This is the same ordering as in m.par.
yhat is the predicted value of the output, according to the current model, i.e.,
row k of yhat contains the predicted value of y(k) based on all past data.
The actual algorithm is selected with the two arguments adg and adm. These
are described under rarx.
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rpem
The input argument th0 contains the initial value of the parameters;, a row
vector, consistent with the rows of thm. (See above.) The default value of th0 is
all zeros.
The arguments P0 and P are the initial and final values, respectively, of the
scaled covariance matrix of the parameters. See rarx. The default value of P0
is 104 times the unit matrix. The arguments phi0, psi0, phi, and psi contain
initial and final values of the data vector and the gradient vector, respectively.
The sizes of these depend in a rather complicated way on the chosen model
orders. The normal choice of phi0 and psi0 is to use the outputs from a
previous call to rpem with the same model orders. (This call could be a dummy
call with default input arguments.) The default values of phi0 and psi0 are all
zeros.
Note that the function requires that the delay nk is larger than 0. If you want
nk=0 , shift the input sequence appropriately and use nk=1.
Algorithm
The general recursive prediction error algorithm (11.44) of Ljung (1999) is
implemented. See also “Recursive Parameter Estimation” on page 3-78 in the
“Tutorial” chapter.
For the special cases of ARX/AR models, and of single-input ARMAX/ARMA,
Box-Jenkins, and Output-Error models, it is more efficient to use rarx,
rarmax, rbj, and roe.
See Also
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pem, rarmax, rarx, rbj, roe, rplr, nkshift
rplr
Purpose
4rplr
Estimate recursively the parameters of a general multi-input single-output
linear model.
Syntax
thm = rplr(z,nn,adm,adg)
[thm,yhat,P,phi] = rplr(z,nn,adm,adg,th0,P0,phi0)
Description
This is a direct alternative to rpem and has essentially the same syntax. See
rpem for an explanation of the input and output arguments.
rplr differs from rpem in that it uses another gradient approximation. See
Section 11.5 in Ljung (1999). Several of the special cases are well known
algorithms.
When applied to ARMAX models, (nn = [na nb nc 0 0 nk]), rplr gives the
Extended Least Squares (ELS) method. When applied to the output error
structure (nn = [0 nb 0 0 nf nk]) the method is known as HARF in the
adm = 'ff' case and SHARF in the adm = 'ng' case.
rplr can also be applied to the time-series case in which an ARMA model is
estimated with
z = y
nn = [na nc]
You can thus use rplr as an alternative to rarmax for this case.
See Also
pem, rarmax, rarx, rbj, roe, rpem
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segment
Purpose
4segment
Segment data and estimate models for each segment.
Syntax
segm = segment(z,nn)
[segm,V,thm,R2e] = segment(z,nn,R2,q,R1,M,th0,P0,ll,mu)
Description
segment builds models of AR, ARX, or ARMAX/ARMA type,
A ( q )y ( t ) = B ( q )u ( t – nk ) + C ( q )e ( t )
assuming that the model parameters are piece-wise constant over time. It
results in a model that has split the data record into segments over which the
model remains constant. The function models signals and systems that may
undergo abrupt changes.
The input-output data are contained in z, which is either an iddata object or a
matrix z = [y u] where y and u are column vectors. If the system has several
inputs, u has the corresponding number of columns.
The argument nn defines the model order. For the ARMAX model
nn = [na nb nc nk]
where na, nb, and nc are the orders of the corresponding polynomials. See
“Polynomial Representation of Transfer Functions” on page 3-11 in the
“Tutorial” chapter. Moreover nk is the delay. If the model has several inputs,
nb and nk are row vectors, giving the orders and delays for each input.
For an ARX model (nc = 0) enter
nn = [na nb nk]
For an ARMA model of a time series
z = y
nn = [na nc]
and for an AR model
nn = na
The output argument segm is a matrix, whose k-row contains the parameters
corresponding to time k. This is analogous to the output argument thm in rarx
and rarmax. The output argument thm of segment contains the corresponding
model parameters that have not yet been segmented. That is, they are not
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segment
piecewise constant, and therefore correspond to the outputs of the functions
rarmax and rarx. In fact, segment is an alternative to these two algorithms,
and has a better capability to deal with time variations that may be abrupt.
The output argument V contains the sum of the squared prediction errors of the
segmented model. It is a measure of how successful the segmentation has
been.
The input argument R2 is the assumed variance of the innovations e(t) in the
model. The default value of R2, R2 = [], is that it is estimated. Then the output
argument R2e is a vector whose k-th element contains the estimate of R2 at time
k.
The argument q is the probability that the model undergoes at an abrupt
change at any given time. The default value is 0.01.
R1 is the assumed covariance matrix of the parameter jumps when they occur.
The default value is the identity matrix with dimension equal to the number
of estimated parameters.
M is the number of parallel models used in the algorithm (see below). Its default
value is 5.
th0 is the initial value of the parameters. Its default is zero. P0 is the initial
covariance matrix of the parameters. The default is 10 times the identity
matrix.
ll is the guaranteed life of each of the models. That is, any created candidate
model is not abolished until after at least ll time steps. The default is ll = 1.
Mu is a forgetting parameter that is used in the scheme that estimates R2. The
default is 0.97.
The most critical parameter for you to choose is R2. It is usually more robust to
have a reasonable guess of R2 than to estimate it. Typically, you need to try
different values of R2 and evaluate the results. (See the example below.)
sqrt(R2) corresponds to a change in the value y(t) that is normal, giving no
indication that the system or the input might have changed.
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segment
Algorithm
The algorithm is based on M parallel models, each recursively estimated by an
algorithm of Kalman filter type. Each is updated independently, and its
posterior probability is computed. The time varying estimate thm is formed by
weighting together the M different models with weights equal to their posterior
probability. At each time step the model (among those that have lived at least
11 samples) that has the lowest posterior probability is abolished. A new model
is started, assuming that the system parameters have jumped, with probability
q, a random jump from the most likely among the models. The covariance
matrix of the parameter change is set to R1.
After all the data are examined, the surviving model with the highest posterior
probability is tracked back and the time instances where it jumped are marked.
This defines the different segments of the data. (If no models had been
abolished in the algorithm, this would have been the maximum likelihood
estimates of the jump instances.) The segmented model segm is then formed by
smoothing the parameter estimate, assuming that the jump instances are
correct. In other words, the last estimate over a segment is chosen to represent
the whole segment.
Examples
Check how the algorithm segments a sinusoid into segments of constant levels.
Then use a very simple model y(t) = b1 * 1, where 1 is a fake input and b 1
describes the piecewise constant level of the signal y(t) (which is simulated as
a sinusoid).
y = sin([1:50]/3)';
thm = segment([y,ones(y)],[0 1 1],0.1);
plot([thm,y])
By trying various values of R2 (0.1 in the above example), more levels are
created as R2 decreases.
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selstruc
Purpose
4selstruc
Select model order (structure).
Syntax
nn = selstruc(v)
[nn,vmod] = selstruc(v,c)
Description
selstruc is a function to help choose a model structure (order) from the
information contained in the matrix v obtained as the output from arxstruc or
ivstruc.
The default value of c is 'plot'. The plot shows the percentage of the output
variance that is not explained by the model, as a function of the number of
parameters used. Each value shows the best fit for that number of parameters.
By clicking in the plot you can examine which orders are of interest. By clicking
on ‘Select’ the variable nn is returned in the workspace as the optimal model
structure for your choice of number of parameters. Several choices can be
made.
c = 'aic' gives no plots, but returns in nn the structure that minimizes
Akaike’s Information Criterion (AIC),
2d
V mod = V  1 + -------

N
where V is the loss function, d is the total number of parameters in the
structure in question, and N is the number of data points used for the
estimation.
c = 'mdl' returns in nn the structure that minimizes Rissanen’s Minimum
Description Length (MDL) criterion.
d log ( N )
V mod = V  1 + ----------------------

N 
When c equals a numerical value, the structure that minimizes
cd
V mod = V  1 + ------

N
is selected.
The output argument vmod has the same format as v, but it contains the
logarithms of the accordingly modified criteria.
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selstruc
Example
4-156
V = arxstruc(data(1:200),data(201:400),struc(1:10,1:10,1:10))
nn = selstruc(V,0); %best fit to validation data
m = arx(data,nn)
set
Purpose
4set
Set or modify the properties of models and iddata sets.
Syntax
set(m,'Property',Value)
set(m,'Property1',Value1,...'PropertyN',ValueN)
set(m,'Property')
set(m)
Description
set is used to set or modify the properties of any of the objects in the toolbox
(iddata, idmodel, idgrey, idarx, idpoly, idss). See the corresponding
reference entries for a complete list of properties.
set(m,'Property',Value) assigns the value Value to the property of the
object m, specified by the string ’Property'. This string can be the full property
name (e.g., ’SSParameterization') or any unambiguous case-insensitive
abbreviation (e.g., ’ss').
set(m,’Property1',Value1,...’PropertyN',ValueN) sets multiple properties
with a single statement. In certain cases this may be necessary, since the model
m must, e.g., have state-space matrices of consistent dimensions after each set
statement.
set(m,’Property') displays admissible values for the property specified by
'Property'.
set(m) displays all assignable values of m and their admissible values.
The same result is also obtained by subassignment.
m.Property = Value
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setpname
Purpose
4setpname
Assign mnemonic parameter names to black box model structures.
Syntax
model = setpname(model)
Description
model is an idmodel object of idarx, idpoly or idss type. The returned model
has the ‘PName’ property set to a cell array of strings that correspond to the
symbols used in this manual to describe the parameters.
For single input idpoly models, the parameters are called 'a1',
'a2', ...,'fn', just as defined in Section “Polynomial Representation of
Transfer Functions” on page 3-11.
For multi-input idpoly models, the b- and f-parameters have the output/input
channel number in parenthesis as in 'b1(1,2)', 'f3(1,2)' etc.
For idarx models, the parameter names are as in 'Ar(ky,ku)' for the ky-ku
entry of the matrix in (Equation 3-46) and similarly for the B-parameters.
For idss models the parameters are named for the matrix entries they
represent, like 'A(4,5)', 'K(2,3)' etc.
This function is particularly useful when certain parameters are to be fixed.
See the property 'FixedParameter' under Algorithm Properties.
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sim
Purpose
4sim
Simulate linear models.
Syntax
y = sim(m,ue)
[y, ysd] = sim(m,ue,init)
Description
m is an arbitrary idmodel object.
ue is an iddata object, containing inputs only. The number of input channels
in ue must either be equal to the number of inputs of the model m, or equal to
the sum of the number of inputs and noise sources (= number of outputs). In
the latter case the last inputs in ue are regarded as noise sources and a
noise-corrupted simulation is obtained. The noise is scaled according to the
property m.NoiseVariance in m, so in order to obtain the right noise level
according to the model, the noise inputs should be white noise with zero mean
and unit covariance matrix. If no noise sources are contained in ue, a noise-free
simulation is obtained.
sim returns y containing the simulated output, as an iddata object.
init gives access to the initial states:
• init = 'm' (default) uses the model m's internally stored initial state.
• init = 'z' uses zero initial state.
• init = x0, where x0 is a column vector of appropriate length uses this value
as the initial state.
The second output argument ysd is the standard deviation of the simulated
output.
If m is a continuous-time model, it is first converted to discrete time with the
sampling interval given by ue taking into account the intersample behavior of
the input (ue.InterSample). See the section“Discrete and Continuous Time
Models” on page 3-60 in the “Tutorial” chapter.
Examples
Simulate a given system m0 (for example created by idpoly).
e
u
y
z
=
=
=
=
iddata([],randn(500,1));
iddata([],idinput(500,'prbs'));
sim(m0,[u e]);
[y u]; % An iddata object with y as output and u as input.
4-159
sim
Validate a model by comparing a measured output y with one simulated using
an estimated model m.
yh = sim(m,u);
plot(y,yh)
See Also
4-160
iddata, idpoly, idarx, idss, idgrey, simsd
simsd
Purpose
4simsd
Simulate models with uncertainty.
Syntax
simsd(m,u)
simsd(m,u,N,noise,Ky)
Description
u is an iddata object containing the inputs. m is a model given as any idmodel
object. N random models are created, according to the covariance information
given in m. The responses of each of these models to the input u are computed
and graphed in the same diagram. If noise = 'noise', noise is added to the
simulation, in accordance with the noise model of m, and its own uncertainty.
Ky denotes the output numbers to be plotted (default all)
The default values are
N = 10
noise = 'nonoise'
Examples
Plot the step response of the model m and evaluate how it varies in view of the
model's uncertainty.
step1 = [zeros(5,1); ones(20,1)];
simsd(m,step1)
See Also
sim
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size
Purpose
4size
Dimensions of iddata. idfrd and idmodel objects.
Syntax
d = size(m)
[ny,nu,Npar,Nx] = size(model)
[N, ny, nu, Nexp] = size(data)
ny = size(data,2)
ny = size(data,'ny')
size(model)
Description
size describes the dimensions of both model and iddata objects.
For iddata objects, the sizes returned are, in this order:
N = the length of the data record. For multiexperiment data, N is a row vector
with as many entries as there are experiments.
ny = the number of output channels
ny = the number of input channels
Ne = the number of experiments
To access just one of these sizes use size(data,k) for the k-th dimension or
size(data,’N'), size(data,’ny'), etc.
When called with one output argument d = size(data) returns:
d = [N ny nu] if the number of experiments is 1
d = [sum(N) ny nu Ne] if the number of experiments is Ne>1
For idmodel objects the sizes returned are, in this order:
ny = the number of output channels
nu = the number of input channels
Npar = the length of the ParameterVector (= the number of estimated
parameters)
Nx = the number of states for idss and idgrey models.
Also in this case the individual dimensions are obtained by size(mod,2),
size(mod,’Npar'), etc.
When size is called with one output argument d = size(mod), d is given by
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size
[ny nu Npar]
For idfrd models, the sizes are:
ny = number of output channels
nu = number of input channels
Nf = number of frequencies
Ns = number of spectrum channels
Also in this case the individual dimensions are obtained by size(mod,2),
size(mod,'Nf'), etc.
When size is called with one output argument d = size(fre), d is given by
[ny nu Nf]
When size is called with no output arguments in any of these cases, the
information is displayed in the MATLAB command window.
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spa
Purpose
4spa
Estimate frequency response and spectrum by spectral analysis.
Syntax
g = spa(data)
g = spa(data,M,w,maxsize)
[g,phi,spe] = spa(data)
Description
spa estimates the transfer function g and the noise spectrum Φ v of the general
linear model
y ( t ) = G ( q )u ( t ) + v ( t )
where Φ v ( ω ) is the spectrum of v ( t ) .
data contains the output-input data as an iddata object. The data may be
complex-valued.
g is returned as an idfrd object (see idfrd) with the estimate of G ( eiω ) at the
frequencies ω specified by row vector w. The default value of w is
w = [1:128]/128∗pi/Ts
Here Ts is the sampling interval of data.
g also includes information about the spectrum estimate of Φ v ( ω ) at the same
frequencies. Both outputs are returned with estimated covariances, included in
g. See idfrd.
M is the length of the lag window used in the calculations. The default value is
M = min(30,length(data)/10)
Changing the value of M exchanges bias for variance in the spectral estimate.
As M is increased, the estimated functions show more detail, but are more
corrupted by noise. The sharper peaks a true frequency function has, the
higher M it needs. See etfe as an alternative for narrowband signals and
systems. See also “Estimating Spectra and Frequency Functions” on page 3-15
in the “Tutorial” chapter.
maxsize controls the memory-speed trade-off (see Algorithm Properties).
For time series, where data contains no input channels, g is returned with the
estimated output spectrum and its estimated standard deviation.
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spa
When spa is called with two or three output arguments:
• g is returned as an idfrd model with just the estimated frequency response
from input to output and its uncertainty.
• phi is returned as an idfrd model, containing just the spectrum data for the
output spectrum Φ v ( ω ) and its uncertainty.
• spe is returned as an idfrd model containing spectrum data for all
output-input channels in data. That is if z = [data.OutputData,
data.InputData], spe contains as spectrum data the matrix-valued power
spectrum of z.
M
S =
∑
Ez ( t + m )z ( t )′ exp ( – iWmT ) win ( m )
m = –M
Here win(m) is weight at lag m of an M-size Hamming window and W is the
frequency value i rad/s. Note that ’ denotes complex-conjugate transpose.
The normalization of the spectrum differs from the one used by spectrum in the
Signal Processing Toolbox. See “Spectrum Normalization and the Sampling
Interval” on page 3-94 in the “Tutorial” chapter for a more precise definition.
Examples
With default frequencies
g = spa(z);
bode(g)
With logarithmically spaced frequencies
w = logspace(-2,pi,128);
g= spa(z,[],w); % (empty matrix gives default)
bode(g,3)
bode(g('noise'),3) % The noise spectrum with confidence interval
of 3 standard deviations.
4-165
spa
Algorithm
The covariance function estimates are computed using covf. These are
multiplied by a Hamming window of lag size M and then Fourier transformed.
The relevant ratios and differences are then formed. For the default
frequencies, this is done using FFT, which is more efficient than for
user-defined frequencies. For multi-variable systems, a straightforward
for-loop is used.
Note that M = γ is in Table 6.1 of Ljung (1999). The standard deviations are
computed as on pages 184 and 188 in Ljung (1999).
See Also
4-166
bode, etfe, ffplot, idfrd, nyquist
ss, tf, zpk, frd
Purpose
4ss, tf, zpk, frd
Transform the model objects of the System Identification Toolbox to the LTI
models of the Control System Toolbox.
Syntax
sys
sys
sys
sys
Description
mod is any idmodel object: idgrey, idarx, idpoly, idss, idmodel, or idfrd.
However, idfrd objects can only be converted to frd objects.
=
=
=
=
ss(mod)
tf(mod)
zpk(mod)
frd(mod)
sys is returned as the indicated LTI model. The noise input channels in mod are
treated as follows.
Consider a model mod with both measured input channels u (nu channels) and
noise channels e (ny channels) with covariance matrix Λ
y = Gu + He
cov ( e ) = Λ = LL′
where L is a lower triangular matrix. Note that mod.NoiseVariance = Λ . The
model can also be described with unit variance, normalized noise source v.
y = Gu + HLv
cov ( v ) = I
For ss, tf, and zpk, both measured input channels u and normalized noise
input channels v in mod will be input channels in sys. The noise input channels
will belong to the InputGroup 'Noise', while the others belong to the
InputGroup 'Measured'. The names of the noise input channels will be
[email protected], where yname is the name of the corresponding output channel. This
means that the LTI object realizes the transfer function [G HL].
For frd, no noise input information is included.
To transform only the measured input channels in sys, use
sys = ss(mod('m'))
and analogously for tf and zpk. This will give a representation of just G.
4-167
ss, tf, zpk, frd
For a time series, (no measured input channels, nu = 0), the LTI
representations in ss, tf, zpk, and frd contain the transfer functions from the
normalized noise sources v to the outputs., that is HL. If the model m has both
measured and noise inputs, sys = ss(mod('n')) will give a representation of
the additive noise. For frd no output is given for a time-series model.
In addition, the normal subreferencing can be used.
sys = ss(mod(1,[3 4]))
If you want to describe [G H] or H (unnormalized noise), from e to y, use first
mod = noisecnv(mod)
to convert the noise channels e to regular input channels. These channels will
have the names [email protected]
4-168
ssdata
Purpose
4ssdata
Transform a model to state-space form.
Syntax
[A,B,C,D,K,X0] = ssdata(m)
[A,B,C,D,K,X0,dA,dB,dC,dD,dK,dX0] = ssdata(m)
Description
m is the model given as any idmodel object. A, B, C, D,K, and X0 are the matrices
in the state-space description
x̃ ( t ) = Ax ( t ) + Bu ( t ) + Ke ( t )
x ( 0 ) = x0
y ( t ) = Cx ( t ) + Dx ( t ) + e ( t )
where x̃ ( t ) is x· ( t ) or x ( t + Ts ) depending on whether m is a continuous or
discrete-time model.
dA, dB, dC, dD, dK, and dX0 are the standard deviations of the state-space
matrices.
If the underlying model itself is a state-space model, the matrices correspond
to the same basis. If the underlying model is an input-output model, an
observer canonical form representation is obtained.
For a time-series model (no measured input channels, u= [ ]), B and D are
returned as the empty matrices.
Subreferencing models in usual way (see idmodel properties) will give the
state-space representation of the chosen channels. Notice in particular that
[A,B,C,D] = ssdata(m('m'))
gives the response from the measured inputs. This is a model without a
disturbance description. Moreover
[A,B,C,D,K] = ssdata(m('n'))
('n' as in “noise”) gives the disturbance description, i.e, a time-series
description of the additive noise with no measured inputs, so that B=[] and
D=[].
4-169
ssdata
To obtain state-space descriptions that treat all input channels, both u and e
as measured inputs, first apply the conversion
m = noisecnv(m)
or
m = noisecnv(m,'norm')
where the latter case first normalizes e to v, where v has a unit covariance
matrix. See the reference page for noisecnv.
Algorithm
The computation of the standard deviations in the input-output case assumes
that an A polynomial is not used together with a F or D polynomial in
(Equation 3-43). For the computation of standard deviations in the case that
the state-space parameters are complicated functions of the parameters, Gauss
approximation formula is used together with numerical derivatives. The
step-sizes for this differentiation are determined by nuderst.
See Also
idmodel, idss, nuderst
4-170
step
Purpose
4step
Estimate/compute/display step response.
Syntax
step(m)
step(data)
step(data,'sd',sd,'PW',na,Time)
step(m,'sd',sd,Time)
step(m1,m2,...,dat1, ...,mN,Time,'sd',sd)
step(m1,'PlotStyle1',m2,'PlotStyle2',...,dat1,'PlotStylek',...,mN,
'PlotStyleN',Time,'sd',sd)
[y,t,ysd] = step(m)
mod = step(data)
Description
step can be applied both to idmodels and to iddata sets, as well as to any
mixture.
For a discrete-time idmodel m, the step response y and, when required, its
estimated standard deviation ysd, is computed using sim. When called with
output arguments, y, ysd and the time vector t are returned. When step is
called without output arguments, a plot of the step response is shown. If sd is
given a value larger than zero, a confidence region around the response is
drawn. It corresponds to the confidence of sd standard deviations. If the input
argument list contains 'fill', this region is plotted as a filled area.
The start time T1 and the end time T2 can be specified by Time= [T1 T2]. If T1
is not given, it is set to -T2/4. The negative time lags (the step is always
assumed to occur at time 0) show possible feedback effects in the data, when
the step is estimated directly from data. If Time is not specified, a default value
is used.
For an iddata set data, step(data) estimates a high order, noncausal FIR
model after first having prefiltered the data so that the input is “as white as
possible.” The step response of this FIR model and, when asked for, its
confidence region is then plotted. When called with an output argument, step,
in the iddata case, returns this FIR model, stored as an idarx model.The order
of the prewhitening filter can be specified as na. The default value is na = 10.
Any number and any mixture of models and data sets can be used as input
arguments. The responses are plotted with each input/output channel (as
defined by the models and data sets InputName and OutputName) as a separate
plot. Colors, linestyles, and marks can be defined by PlotStyle values, as in
4-171
step
step(m1,'b-*',m2,'y--',m3,'g')
The noise input channels in m are treated as follows: Consider a model m with
both measured input channels u (nu channels) and noise channels e (ny
channels) with covariance matrix Λ
y = Gu + He
cov ( e ) = Λ = LL′
where L is a lower triangular matrix. Note that m.NoiseVariance = Λ . The
model can also be described with unit variance, normalized noise source v:
y = Gu + HLv
cov ( v ) = I
• step(m) plots the step response of the transfer function G.
• step(m('n')) plots the step response of the transfer function H. (ny inputs
and ny outputs).The input channels have names [email protected], where yname is the
name of the corresponding output.
• If m is a time series, that is nu = 0, step(m) plots the step response of the
transfer function H.
• step(noisecnv(m)) plots the step response of the transfer function [G H]
(nu+ny inputs and ny outputs). The noise input channels have names
[email protected], where yname is the name of the corresponding output.
• step(noisecnv(m,'norm')) plots the step response of the transfer function
[G HL] (nu+ny inputs and ny outputs). The noise input channels have names
[email protected], where yname is the name of the corresponding output.
Arguments
If step is called with a single idmodel m, the output argument y is a 3-D array
of dimension Nt-by-ny-by-nu. Here Nt is the length of the time vector t, ny is the
number of output channels and nu is the number of input channels. Thus
y(:,ky,ku) is the response in the ky-th output channel to a step in the ku-th
input channel. No plot is produced when output arguments are used.
ysd has the same dimensions as y and contains the standard deviations of y.
If step is called with an output argument and a single data set in the input
arguments, the output is returned as an idarx model mod containing the high
order FIR model, and its uncertainty. By calling step with mod, the responses
can be displayed and returned without having to redo the estimation.
4-172
step
Example
step(data,'sd',3) estimates and plots the step response
mod = step(data)
step(mod,'sd',3)
See Also
cra, impulse
4-173
struc
Purpose
4struc
Generate model structure matrices.
Syntax
NN = struc(NA,NB,NK)
Description
struc returns in NN the set of model structures comprised of all combinations
of the orders and delays given in row vectors NA, NB, and NK. The format of NN
is consistent with the input format used by arxstruc and ivstruc. The
command is intended for single-input systems only.
Examples
The statement
NN = struc(1:2,1:2,4:5);
produces
NN =
1 1
1 1
1 2
1 2
2 1
2 1
2 2
4-174
4
5
4
5
4
5
5
timestamp
Purpose
4timestamp
Bookkeeping of created objects.
Syntax
timestamp(obj)
ts = timestamp(obj)
Description
obj is any idmodel, iddata or idfrd object. timestamp returns or displays a
string with information about when the object was created and last modified.
4-175
tfdata
Purpose
4tfdata
Transform a model to transfer function form.
Syntax
[num,den] = tfdata(m)
[num,den,sdnum,sdden] = tfdata(m)
[num,den,sdnum,sdden] = tfdata(m,'v')
Description
m is a model given as any idmodel object with ny output channels and nu input
channels
num is a a cell array of dimension ny-by-nu. num{ky,ku} (note the curly
brackets) contains the numerator of the transfer function from input ku to
output ky. This numerator is a row vector whose interpretation is described
below.
Similarily den is an ny-by-nu cell array of the denominators.
sdnum and sdden have the same formats as num and den. They contain the
standard deviations of the numerator and denominator coefficients.
If m is a SISO model, adding an extra input argument 'v' (for vector) will
return num and den as vectors rather than cell arrays.
The formats of num and den are the same as those used by the Signal Processing
Toolbox and the Control System Toolbox, both for continuous-time and
discrete-time models. See “Examining Models” in the “Tutorial” chapter and
the examples below.
The noise input channels in m are treated as follows: Consider a model m with
both measured input channels u (nu channels) and noise channels e (ny
channels) with covariance matrix Λ
y = Gu + He
cov ( e ) = Λ = LL′
where L is a lower triangular matrix. Note that m.NoiseVariance = Λ . The
model can also be described with unit variance, normalized noise source v:
y = Gu + HLv
cov ( v ) = I
4-176
tfdata
• tfdata(m) returns the transfer function G.
• tfdata(m('n')) returns the transfer function H. (ny inputs and ny outputs)
• If m is a time series, that is nu = 0, tfdata(m) returns the transfer function H.
• tfdata(noisecnv(m)) returns the transfer function [G H] (nu+ny inputs
and ny outputs)
• tfdata(noisecnv(m,'norm')) returns the transfer function [G HL] (nu+ny
inputs and ny outputs).
Examples
For a continuous-time model
num = [1 2]
den = [1 3 0]
corresponds to the transfer function
s+2
G ( s ) = -----------------2
s + 3s
For a discrete-time model
num = [2 4 0]
den = [1 2 3 5]
corresponds to the transfer function
2
2z + 4z
H ( z ) = -------------------------------------------3
2
z + 2z + 3z + 5
which is the same as
–1
–2
2q + 4q
H ( q ) = -----------------------------------------------------------–1
–2
–3
1 + 2q + 3q + 5q
Note that for discrete time, idpoly and polydata has a different interpretation
of the numerator vector, in case it does not have the same length as the
denominator vector. To avoid confusion, it is advised to fill out with zeros to
make numerator and denominator vectors the same length. That is done by
tfdata.
See Also
idpoly, noisecnv
4-177
view
Purpose
4view
Plot a variety of model characteristics (requires the Control System Toolbox).
Syntax
view(m)
view(m('n'))
view(m1,...,mN,Plottype)
view(m1,PlotStyle1,...,mN,PlotStyleN)
Description
m is the output-input data to be graphed, given as any idfrd or idmodel object.
After appropriate model transformations, the LTI Viewer of the Control
System Toolbox is invoked. This allows e.g., bode, nyquist, impulse, step, and
zero/poles plots.
To compare several models m1,...,mN, use view(m1,...,mN). With
PlotStyle, the color, linestyle and marker, of each model can be specified.
view(m1,'y:*',m2,'b')
Adding Plottype as a last argument specifies the type of plot in which view is
initialized. Plottype is any of 'impulse', 'step', 'bode', 'nyquist',
'nichols', 'sigma', or 'pzmap'. It can also be given as a cell array containing
any collection of these strings (up to 6) in which case a multiplot is shown.
view will not display confidence regions. For that use bode, nyquist, impulse,
step, and pzmap.
The noise input channels in m are treated as follows: Consider a model m with
both measured input channels u (nu channels) and noise channels e (ny
channels) with covariance matrix Λ
y = Gu + He
cov ( e ) = Λ = LL
T
where L is a lower triangular matrix. Note that m.NoiseVariance = Λ . The
model can also be described with unit variance, normalized noise source v:
y = Gu + HLv
cov ( v ) = I
4-178
view
• view(m) plots the characteristics of the transfer function G.
• view(m('n')) plots the characteristics of the transfer function HL. (ny
inputs and ny outputs).The input channels have names [email protected], where
yname is the name of the corresponding output.
• If m is a time series, that is nu = 0, view(m) plots the characteristics of the
transfer function HL.
• view(noisecnv(m)) plots the characteristics of the transfer function [G H]
(nu+ny inputs and ny outputs). The noise input channels have names
[email protected], where yname is the name of the corresponding output.
• view(noisecnv(m,'norm')) plots the characteristics of the transfer
function [G HL] (nu+ny inputs and ny outputs. The noise input channels
have names [email protected], where yname is the name of the corresponding output.
view does not give access to all of the features of ltiview. Use
ml = ss(m), ltiview(Plottype,ml,...)
to reach these options.
See Also
bode, impulse, nyquist, step, pzmap
4-179
zpkdata
Purpose
4zpkdata
Compute zeros, poles, and transfer function gains of models.
Syntax
[z,p,k] = zpkdata(m)
[z,p,k,dz,dp,dk] = zpkdata(m)
[z,p,k,dz,dp,dk] = zpkdata(m,'v')
Description
m is a model given as any idmodel object with ny output channels and nu input
channels.
z is a a cell array of dimension ny-by-nu. z{ky,ku} (note the curly brackets)
contains the zeros of the transfer function from input ku to output ky. This is a
column vector of possibly complex numbers.
Similarily p is an ny-by-nu cell array containing the poles.
k is a ny-by-nu matrix whose ky-ku entry is the transfer function gain of the
transfer function from input ku to output ky. Note that the transfer function
gain is value of the leading coefficient of the numerator, when the leading
coefficient of the denominator is normalized to 1. It thus differs from the static
gain. The static gain can be retrieved as Ks = freqresp(m,0).
dz contains the covariance matrices of the zeros in the following way: dz is a
ny-by-nu cell array. dz{ky,ku} contains the covariance information about the
zeros of the transfer function from ku to ky. It is a 3-D array of dimension
2-by-2-by-Nz, where Nz is the number of zeros. dz{ky,ku}(:,:,kz) is the
covariance matrix of the zero z{ky,ku}(kz), so that the 1-1 element is the
variance of the real part, the 2-2 element is the variance of the imaginary part
and the 1-2 and 2-1 elements contain the covariance between the real and
imaginary parts.
dp contains the covariance matrices of the poles in the same way.
dk is a matrix containing the variances of the elements of k.
If m is a SISO model, adding an extra input argument 'v' (for vector) will
return z and p as vectors rather than cell arrays.
Note that the zeros and the poles are associated with the different channels
combinations. To obtain the so-called transmission zeros, use tzero.
4-180
zpkdata
The noise input channels in m are treated as follows: Consider a model m with
both measured input channels u (nu channels) and noise channels e (ny
channels) with covariance matrix Λ
y = Gu + He
cov ( e ) = Λ = LL′
where L is a lower triangular matrix. Note that m.NoiseVariance = Λ . The
model can also be described with unit variance, normalized noise source v.
y = Gu + HLv
cov ( v ) = I
Then:
• zpkdata(m) returns the zeros and poles of G.
• zpkdata(m('n')) returns the zeros and poles of H. (ny inputs and ny
outputs)
• If m is a time series, that is nu = 0, zpkdata(m) returns the zeros and poles of
H.
• zpkdata(noisecnv(m)) returns the zeros and poles of the transfer function
[G H] (nu+ny inputs and ny outputs)
• zpkdata(noisecnv(m,'norm')) returns the zeros and poles of the transfer
function [G HL] (nu+ny inputs and ny outputs.
The procedure handles both models in continuous and discrete time.
Note that you cannot rely on information about zeros and poles at the origin
and at infinity for discrete time models. (This is a somewhat confusing issue
anyway.)
Algorithm
The poles and zeros are computed using ss2zp. The covariance information is
computed using Gauss’s approximation formula, using the parameter
covariance matrix contained in m. When the transfer function depends on the
parameters in a complicated way, numerical differentiation is applied. The
step-sizes for the differentiation are determined in the M-file nuderst.
4-181
zpkdata
4-182
Index
A
adaptive noise cancelling 4-146
Advanced 4-20
AIC, the Akaike Information Criterion 4-15
Akaike’s Final Prediction Error (FPE) 3-64
AR model 3-26
ARARMAX structure 3-12
ARMAX 2-23
ARMAX model 2-23
ARMAX structure 3-12
ARX 2-23
ARX model 1-7, 2-21, 3-7, 3-11, 3-17, 3-26, 3-36
B
basic tools 3-3
BJ 2-23
Bode diagram 2-30
Bode plot 1-10
Box-Jenkins (BJ) structure 3-12
Box-Jenkins model 2-23
Burg’s method 3-27
C
communication window ident 2-2
comparisons using compare 3-51
complex-valued data 3-98
confidence interval 2-29
correlation analysis 1-4, 2-16, 3-19
covariance function 3-10
covariance matrix 3-28, 3-92
suppressing calculation 3-92
covariance method 3-27
creating models from data 2-2
cross correlation function 1-17, 3-67
cross spectrum 3-15
customized plots 2-37
CVA 4-18
D
data
channels 3-21
feedback 3-66
iddata object 3-18, 3-21
multiple experiments 3-22
representation 3-18
Data Board 2-3
data handling checklist 2-13
data representation 2-7, 3-18
data views 1-4
delays 3-11, 3-38, 3-93
detrending the data 2-11
difference equation 1-7
disturbance 1-6
disturbance spectra 2-30
drift matrix 3-79
dynamic models, introduction 1-6
E
empirical transfer function estimate 3-20
estimation
parametric 3-25
estimation data 1-4
estimation method
instrumental variables 3-17
nonparametric 3-18
parametric 3-25
prediction error approach 2-19, 3-17
subspace method 3-17
estimation methods
I-1
Index
direct 2-15
parametric 2-15
exporting to the MATLAB workspace 2-34
extended least squares (ELS) 3-82
F
fault detection 3-83
feedback 1-15
feedback in data 3-66
FixedParameter 4-18
focus 2-12, 3-31
frequency
function 2-16
functions 3-9, 3-19
plots 3-9
range 3-9
response 2-16
scales 3-9
frequency domain description 3-10
frequency response 1-10, 2-30, 3-19
G
Gauss-Newton direction 4-20
Gauss-Newton minimization 3-28
geometric lattice method 3-27
graphical user interface (GUI) 2-2
greybox-modeling 3-44
GUI 2-2
topics 2-35
H
Hamming window 3-20
I-2
I
idarx model object 3-37
ident window 2-35
identification method
subspace 2-26
identification process, basic steps 1-12
idfrd model object 3-19
idgrey model object 3-44
idpoly model object 3-36
idss model object 3-39
importing data into the GUI 2-9
impulse response 1-10, 2-30, 3-9, 3-10
Information Theoretic Criterion (AIC) 3-64
initial condition 3-28
initial parameter values 3-47
initial state 3-91
in GUI 2-20
state space model 3-41
innovations form 3-13, 3-40
input signals 1-6
instrumental variable 3-17
(IV) method 3-26
technique 3-27
iterative search 3-28, 3-33
K
Kalman gain 3-14, 3-40
L
lag widow 3-16
lag window 3-20
layout 2-36
least squares 2-22
Levenberg-Marquard 4-20
LimitError 4-19
Index
M
main ident window 2-35
maximum likelihood
criterion 3-29
method 3-17
MaxIter 4-19
MaxSize 4-17
memory horizon 3-80
merge experiments 3-23
model
nonparametric 3-11
output-error 1-8
parametric 2-18
properties 1-10
set 1-4
state-space 1-8
structure 2-17, 3-4, 3-35
structure selection 3-4
uncertainty 3-68
view functions 2-28
views 1-8, 2-4
Model Board 2-3
model order 1-7
model structure 1-4
model uncertainty 2-29
model validation 1-5
model views 1-4
MOESP 4-18
multi-output models
criterion 3-29
Multiple experiments 3-22
multivariable ARX model 3-37
multivariable systems 1-18, 3-26
N
N4Horison 4-18
N4Horizon 3-32
N4Weight 3-32
4-18
na,nb,nc,nd,nf
parameter definitions 3-11
noise 1-6
noise model 1-8
noise source 1-8, 2-32
noise-free simulation 1-9
noise-free simulations 3-68
nonequal sampling 3-22
nonparametric estimation 3-18
nonparametric identification 1-4
Normalized Gradient (NG) Approach 3-81
numerical differentiation 3-47
Nyquist plot 3-20
O
OE 2-23
OE model 3-12
offsets 3-74
order editor 2-19
outliers
signals 1-4
output error model 2-23
output signals 1-6
Output-Error model 1-8
output-error model 2-23, 3-12, 3-92
state space model 3-41
P
parametric identification 1-4
parametric model 2-18
parametric model estimation 3-25
periodogram 3-21
I-3
Index
pole 2-31
poles 1-11
poorly damped systems 1-18
prediction
error identification 2-19
error method 3-17
prediction error 3-16
prediction horizon 2-32
preferences
in GUI 2-37
prefiltering 2-12
prefiltering signals 2-11
Q
Quickstart menu item 2-13
spectrum 3-9, 3-10, 3-15, 3-19
startup identification procedure 1-14
state space model 3-39
continuous time 3-14, 3-40
innovations form 3-40
output-error model 3-41
stochastic 3-13
state variables 1-8
state vector 3-13
state-space
model 2-25
state-space model 3-13
state-space models 1-7
step response 1-10, 2-30
structure 1-4
structure matrices 3-42
subspace method 2-26, 3-17
R
recursive
identification 3-4, 3-78
parameter estimation 3-78
references list 1-21
resampling 2-12
residual analysis 1-17, 2-32
residuals 1-2, 2-32
robustification 4-19
S
sampling interval 1-6, 3-36
SearchDirection 4-19
selecting data ranges 2-11
Sessions 2-5
shift operator 2-23, 3-9
simulating data 2-13
spectral analysis 1-4, 3-16, 3-19
I-4
T
time delay 1-7
time domain description 3-10
time series model 3-20, 3-26
time-continuous systems 3-36
Tolerance 4-19
trace 3-28, 4-19
transfer function 1-8, 3-10
transient response 1-10, 2-30
typographical conventions (table) xiv
U
uncertainty
suppressing calculation 3-92
Unnormalized Gradient (UG) Approach 3-81
Index
V
validation data 1-2, 1-4, 2-4
W
white noise 1-9, 3-10
window sizes 3-20
working data 1-4
Working Data set 2-3
Y
Yule-Walker approach 3-27
Z
zero 2-31
zeros 1-11
I-5
Index
I-6
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