Identification and Estimation for Models Described by Differential-Algebraic Equations Markus Gerdin

Identification and Estimation for Models Described by Differential-Algebraic Equations Markus Gerdin
Linköping Studies in Science and Technology. Dissertations.
No. 1046
Identification and Estimation for
Models Described by
Differential-Algebraic Equations
Markus Gerdin
Department of Electrical Engineering
Linköpings universitet, SE–581 83 Linköping, Sweden
Linköping 2006
Identification and Estimation for Models Described by Differential-Algebraic
Equations
c 2006 Markus Gerdin
[email protected]
www.control.isy.liu.se
Division of Automatic Control
Department of Electrical Engineering
Linköpings universitet
SE–581 83 Linköping
Sweden
ISBN 91-85643-87-4
ISSN 0345-7524
Printed by LiU-Tryck, Linköping, Sweden 2006
To Jessica
Abstract
Differential-algebraic equations (DAEs) form the natural way in which models of physical
systems are delivered from an object-oriented modeling tool like Modelica. Differentialalgebraic equations are also known as descriptor systems, singular systems, and implicit
systems. If some constant parameters in such models are unknown, one might need to
estimate them from measured data from the modeled system. This is a form of system
identification called gray box identification. It may also be of interest to estimate the
value of time-varying variables in the model. This is often referred to as state estimation.
The objective of this work is to examine how gray box identification and estimation of
time-varying variables can be performed for models described by differential-algebraic
equations.
If a model has external stimuli that are not measured or uncertain measurements, it
is often appropriate to model this as stochastic processes. This is called noise modeling. Noise modeling is an important part of system identification and state estimation,
so we examine how well-posedness of noise models for differential-algebraic equations
can be characterized. For well-posed models, we then discuss how particle filters can
be implemented for estimation of time-varying variables. We also discuss how constant
parameters can be estimated.
When estimating time-varying variables, it is of interest to examine if the problem is
observable, that is, if it has a unique solution. The corresponding property when estimating constant parameters is identifiability. In this thesis, we discuss how observability and
identifiability can be determined for DAEs. We propose three approaches, where one can
be seen as an extension of standard methods for state-space systems based on rank tests.
For linear DAEs, a more detailed analysis is performed. We use some well-known
canonical forms to examine well-posedness of noise models and to implement estimation
of time-varying variables and constant parameters. This includes formulation of Kalman
filters for linear DAE models. To be able to implement the suggested methods, we show
how the canonical forms can be computed using numerical software from the linear algebra package LAPACK.
v
Acknowledgments
There are several people who helped me during the work with this thesis. First of all I
would like to thank my supervisors Professor Torkel Glad and Professor Lennart Ljung
for guiding me in my research in an excellent way and always taking time to answer my
questions. It has been a privilege to have you both by my side during my time as a Ph.D.
student.
Furthermore, I would like to thank everyone at the Control and Communication group
for providing a nice working atmosphere. I am going to miss the coffee room discussions.
I would like to mention Johan Sjöberg for many enlightening discussions on DAE models and the cooperation on noise modeling for nonlinear DAE models, and Dr. Thomas
Schön for the cooperation on the work on noise modeling for linear DAE. This thesis has
been proofread by Gustaf Hendeby, Dr. Jacob Roll, Dr. Thomas Schön, Johan Sjöberg,
and Henrik Tidefelt. You all helped improve the quality of the thesis. I also thank Ulla
Salaneck for helping with many practical issues, always with a cheerful attitude.
This work has been supported by the Swedish Foundation for Strategic Research
(SSF) through VISIMOD and ECSEL and by the Swedish Research Council (VR) which
is gratefully acknowledged.
Finally I would like to thank my family and friends for inspiration and support. You
are important to me, even though I might have neglected you during the work with this
thesis. Finally I thank Jessica for understanding when I had to focus on writing this thesis.
You are an important part of my life.
vii
Contents
1
Introduction
1.1 Problem Formulation . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1.2 Outline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1.3 Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2
Modeling
2.1 Introduction: Component-Based Modeling . . . . .
2.1.1 Deterministic Models . . . . . . . . . . . .
2.1.2 Stochastic Models . . . . . . . . . . . . .
2.2 Nonlinear DAE Models . . . . . . . . . . . . . . .
2.3 Linear DAE Models . . . . . . . . . . . . . . . . .
2.3.1 Introduction . . . . . . . . . . . . . . . . .
2.3.2 Regularity . . . . . . . . . . . . . . . . . .
2.3.3 A Canonical Form . . . . . . . . . . . . .
2.3.4 Alternative Canonical Forms . . . . . . . .
2.3.5 State-Space Form . . . . . . . . . . . . . .
2.3.6 Sampling . . . . . . . . . . . . . . . . . .
2.4 Linear Time-Varying DAE Models . . . . . . . . .
2.5 DAE Solvers . . . . . . . . . . . . . . . . . . . .
2.6 Linear Difference-Algebraic Equations . . . . . . .
2.6.1 Regularity . . . . . . . . . . . . . . . . . .
2.6.2 A Canonical Form . . . . . . . . . . . . .
2.6.3 State-Space Form . . . . . . . . . . . . . .
2.7 Stochastic Models . . . . . . . . . . . . . . . . . .
2.7.1 Stochastic Processes . . . . . . . . . . . .
2.7.2 Continuous-Time Linear Stochastic Models
2.7.3 Discrete-Time Linear Stochastic Models . .
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1
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x
Contents
2.8
3
2.7.4 Nonlinear Stochastic Models . . . . . . . . . . . . . . . . . . . .
Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
System Identification
3.1 Prediction Error Methods . . . . .
3.2 The Maximum Likelihood Method
3.3 Frequency Domain Identification .
3.4 Identifiability . . . . . . . . . . .
3.5 Observability . . . . . . . . . . .
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I
Nonlinear DAE Models
4
Well-Posedness of Nonlinear Estimation Problems
4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . .
4.2 Literature Overview . . . . . . . . . . . . . . . . . . . . .
4.3 Background and Motivation . . . . . . . . . . . . . . . .
4.4 Main Results . . . . . . . . . . . . . . . . . . . . . . . .
4.5 Particle Filtering . . . . . . . . . . . . . . . . . . . . . .
4.6 Implementation Issues . . . . . . . . . . . . . . . . . . .
4.7 Example: Dymola Assisted Modeling and Particle Filtering
4.8 Parameter Estimation . . . . . . . . . . . . . . . . . . . .
4.9 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . .
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5
Identifiability and Observability for DAEs Based on Kunkel and Mehrmann 87
5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87
5.2 Identifiability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88
5.3 Observability Tests Based on Kunkel and Mehrmann . . . . . . . . . . . 89
5.4 Identifiability Tests based on Kunkel and Mehrmann . . . . . . . . . . . 90
5.5 Application to State-Space Models . . . . . . . . . . . . . . . . . . . . . 93
5.6 Other Insights Using Kunkel’s and Mehrmann’s Theory . . . . . . . . . . 99
5.6.1 Observability Indices . . . . . . . . . . . . . . . . . . . . . . . . 99
5.6.2 Zero Dynamics . . . . . . . . . . . . . . . . . . . . . . . . . . . 101
5.7 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104
6
Identifiability Tests Using Differential Algebra for Component-Based Models105
6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105
6.2 Main Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108
6.2.1 Global Identifiability . . . . . . . . . . . . . . . . . . . . . . . . 109
6.2.2 Local Identifiability . . . . . . . . . . . . . . . . . . . . . . . . . 110
6.3 Applying the Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . 110
6.4 Examples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111
6.5 A Mechanics Model Library . . . . . . . . . . . . . . . . . . . . . . . . 113
6.6 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117
xi
7
II
8
9
Simulation-Based Tests for Identifiability
7.1 Introduction . . . . . . . . . . . . . . . . . . .
7.2 Basic Setup . . . . . . . . . . . . . . . . . . .
7.3 Examining Identifiability . . . . . . . . . . . .
7.3.1 Preprocessing . . . . . . . . . . . . . .
7.3.2 Drawing Conclusions on Identifiability
7.3.3 Identifiable Functions of Parameters . .
7.4 Example . . . . . . . . . . . . . . . . . . . . .
7.5 Conclusions and Ideas For Extensions . . . . .
7.5.1 Initialization for Identification . . . . .
7.5.2 Non-Minimum Phase Systems . . . . .
7.5.3 Trajectory Generation . . . . . . . . .
7.5.4 Observability . . . . . . . . . . . . . .
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Linear DAE Models
Linear SDAE Models
8.1 Introduction . . . . . . . . . . . . . .
8.2 Noise Modeling . . . . . . . . . . . .
8.2.1 Time Domain Derivation . . .
8.2.2 Frequency Domain Derivation
8.3 Example . . . . . . . . . . . . . . . .
8.4 Sampling with Noise Model . . . . .
8.5 Kalman Filtering . . . . . . . . . . .
8.6 Time-Varying Linear SDAE Models .
8.7 Difference-Algebraic Equations . . .
8.7.1 Noise Modeling . . . . . . . .
8.7.2 Kalman Filtering . . . . . . .
8.8 Conclusions . . . . . . . . . . . . . .
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150
Well-Posedness of Parameter Estimation Problems
9.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
9.2 Problem Formulation . . . . . . . . . . . . . . . . . . . . . . . . .
9.3 Main Result . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
9.4 Measuring Signals with Infinite Variance . . . . . . . . . . . . . . .
9.5 The Log-Likelihood Function and the Maximum Likelihood Method
9.6 Frequency Domain Identification . . . . . . . . . . . . . . . . . . .
9.7 Time-Varying Linear SDAE Models . . . . . . . . . . . . . . . . .
9.8 Difference-Algebraic Equations . . . . . . . . . . . . . . . . . . .
9.8.1 Time Domain Identification . . . . . . . . . . . . . . . . .
9.8.2 Frequency Domain Identification . . . . . . . . . . . . . .
9.9 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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xii
Contents
10 Well-Posedness of State Estimation Problems
10.1 Introduction . . . . . . . . . . . . . . . . . . . . . .
10.2 Formulations without Continuous-Time White Noise
10.3 Formulations with Continuous-Time White Noise . .
10.4 Example . . . . . . . . . . . . . . . . . . . . . . . .
10.5 Time-Varying Linear SDAE Models . . . . . . . . .
10.6 Conclusions . . . . . . . . . . . . . . . . . . . . . .
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11 Implementation Issues
11.1 Introduction . . . . . . . . . . . . .
11.2 Generalized Eigenvalues . . . . . .
11.3 Computation of the Canonical Forms
11.4 Summary . . . . . . . . . . . . . .
11.5 Application Example . . . . . . . .
11.6 Difference-Algebraic Equations . .
11.7 Conclusions . . . . . . . . . . . . .
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12 Initialization of Parameter Estimates
12.1 Introduction . . . . . . . . . . . . . . . .
12.2 Transforming the Problem . . . . . . . .
12.2.1 The Case of Invertible E(θ) . . .
12.2.2 The Case of Non-Invertible E(θ)
12.3 Sum of Squares Optimization . . . . . . .
12.4 Difference-Algebraic Equations . . . . .
12.5 Conclusions . . . . . . . . . . . . . . . .
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13 Conclusions
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A Notation
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B Proof of Theorem 9.1
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Bibliography
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Index
207
1
Introduction
Modeling of physical systems is a fundamental problem within the engineering sciences.
Examples of physical systems that can be modeled are the weather, a human cell and an
electrical motor. Models of these systems can differ greatly in complexity. For example,
a model of the weather could be everything from a statement like “if it is sunny today, it is
probably sunny tomorrow too” to a complex mathematical model used by meteorologists.
Although models differ greatly in complexity, they have in common that they can be used
to make predictions. A model of the weather could be used to make weather forecasts,
a model of a human cell could be used to predict how it will react to different drugs,
and a model of an electrical motor could be used to predict the effect of applying a certain
voltage. In this thesis we will discuss mathematical models, that is equations that describe
the behavior of a system. Such models can be constructed in different ways. One method
is to use well-known physical relations, such as Newton’s and Kirchhoff’s laws. We will
call this physical modeling. Another way is to estimate a model using measurements
from the system. This is called system identification. For the electrical motor, we could
for example measure the applied voltage and the resulting angle on the axis of the motor
and estimate a model from that. A third case, which is a combination of the two previous
modeling methods, is when we have constructed a model using physical relations but do
not know the values of certain parameters in the model. These parameters could then be
estimated using measurements from the system even if we cannot measure them directly.
We will refer to this as gray-box identification.
Traditionally, physical modeling has been performed by manually writing down the
equations that describe the system. If gray-box identification is necessary, the equations
must be transformed manually into a suitable form. The manual modeling has today
partly been replaced by tools that automate the physical modeling process. They both
include tools for modeling systems within a certain domain, such as electrical systems,
and general modeling tools that allow modeling of systems that contain components from
different domains. An example of an object-oriented modeling language for multi-domain
1
2
1
Introduction
modeling is Modelica (Fritzson, 2004; Tiller, 2001). These tools can greatly simplify the
modeling task. In this thesis we will examine how gray-box identification in models
generated by a modeling tool can be automated.
When a model has been constructed, it can be used to predict the future behavior of
the modeled system and to estimate the values of variables that are not measured. We
will thus also examine how models created using tools such as Modelica can be used for
estimation and prediction.
1.1
Problem Formulation
In the most general setting, we would like to estimate parameters or unmeasured variables
in a collection of equations that has been created by a modeling tool. These equations
relate a vector of internal variables, x(t), that vary with time, and their derivatives with
respect to time, ẋ(t), to inputs to the system u(t). Here t denotes dependence on time.
In the equations, there may be some unknown parameters θ that are to be estimated.
An output y(t) from the system is measured. The relationships can be described by the
equations
F ẋ(t), x(t), u(t), t, θ = 0
(1.1a)
y(t) = h x(t), θ .
(1.1b)
This is called a differential-algebraic equation, or DAE (Brenan et al., 1996; Dai, 1989b;
Kunkel and Mehrmann, 2001, 2006). DAEs are also known as descriptor systems, singular systems, and implicit systems. The discussion in this thesis concerns how the unknown
constant parameters θ and unknown time-dependent variables x(t) can be estimated using measurements of the input u(t) and the output y(t). Special attention will be given to
how these problems can be approached by modeling disturbances acting on the system as
stochastic processes.
Below we provide an example of a DAE. For this example it would be possible to
transform the system into a form suitable for identification and estimation manually, but
it would be much more convenient if the identification software could handle the DAE
system directly. A form suitable for system is for example a state-space model, where
(1.1a) takes the form ẋ(t) = f x(t), u(t), t, θ .
Example 1.1: DAE model
Consider a cart which is driven forward by an electrical motor connected to one pair of
the wheels. The parameters of a model of the system are the mass m, the radius of the
wheels r, the torque constant of the motor k, the resistance R, the inductance L of the
motor coil, and the coefficient b representing resistance caused by the air. The internal
variables describing the system are the velocity of the cart v(t), the acceleration of the
cart a(t), the force between the wheels and the ground F (t), the torque from the motor
M (t), the angular velocity of the motor axis ω(t), some voltages in the motor, uL (t),
uR (t), and ug (t), and the current I(t). The input to the system is the voltage u(t). If
this system is modeled with a modeling tool such as in Figure 1.1, we get a collection of
1.1
3
Problem Formulation
R
L
r
u
m
b
k
Figure 1.1: A model produced using the modeling tool Modelica.
equations describing the system, e.g.:
F (t) = rM (t)
dv(t)
= a(t) − bv 2 (t)
dt
rω(t) = v(t)
M (t) = kI(t)
ug (t) = kω(t)
F (t) = ma(t)
uR (t) = RI(t)
uL (t) = L
(1.2)
u(t) = uL (t) + ug (t) + uR (t)
dI(t)
dt
These equations could automatically be rewritten in the form (1.1). However, it would
be tedious work to transform the equations into a form suitable for identification and
estimation if we wanted the estimate one or more of the parameters or internal variables.
How this process can be automated is one of the problems discussed in this thesis. Another
problem which is discussed is how stochastic processes can be included in the equations
to model disturbances acting on the system.
An important special case is when F and h in the DAE (1.1) are linear functions:
E(θ)ẋ(t) = J(θ)x(t) + K(θ)u(t)
y(t) = L(θ)x(t)
(1.3a)
(1.3b)
where E(θ), J(θ), K(θ), and L(θ) are matrices that contain unknown parameters θ that
are to be estimated. The linear DAE (1.3) is also referred to as a linear descriptor system,
a linear singular system and a linear implicit system. In the case with linear equations,
analysis of system properties and other methods are better developed and easier to implement. The discrete-time counterpart of (1.3), where ẋ(t) is replaced by x(t + 1), will also
be discussed.
4
1
1.2
Introduction
Outline
The purpose of this thesis is to describe how unknown parameters and time-dependent
variables in DAE models can be estimated from measurement data. A background on the
models and modeling techniques that are used in the thesis is provided in Chapter 2 and
necessary background information on system identification is presented in Chapter 3. In
the first part of the thesis, nonlinear DAE models are considered. Here noise modeling
and estimation are discussed in Chapter 4 and the system properties identifiability and
observability for DAE models are discussed in Chapter 5, 6, and 7. In the second part of
the thesis, linear DAE models are considered. Noise modeling and estimation is discussed
in Chapters 8, 9, and 10. Following that, implementation of estimation methods for linear
DAE models is discussed in Chapter 11. Initialization of parameter estimates is discussed
in Chapter 12.
The discussion in the thesis is concentrated on continuous-time DAE models, but it is
described how most of the results for linear DAE models can be extended to the discretetime case.
1.3
Contributions
The main contributions of the thesis are:
• The results on noise modeling in nonlinear DAE models and the discussion on how
DAE models can be used in nonlinear particle filtering in Chapter 4.
• The application of the DAE theory by Kunkel and Mehrmann (2001) to examine
observability and identifiability in Chapter 5.
• The results in Chapter 6 concerning how identifiability can be examined in two
stages. If identifiability of the components of a model have been determined, identifiability of the complete model can be examined using a reduced number of equations.
• The results on how identifiability can be examined using DAE solvers in Chapter 7.
• The idea to redefine the input of a linear descriptor system to allow a state-space
description for sampling (Chapter 2).
• The results on noise modeling for linear DAE models and how the noise model can
be used for Kalman filtering and parameter estimation in Chapters 8, 9, and 10.
• The discussion on how the canonical forms for linear DAE models can be computed
with the linear algebra package LAPACK (Chapter 11).
• The result that the parameter initialization problem under certain conditions can be
transformed to the minimization of a biquadratic polynomial (Chapter 12).
The main results in Chapter 4 have been developed in cooperation with Johan Sjöberg
and submitted as
1.3
Contributions
5
M. Gerdin and J. Sjöberg. Nonlinear stochastic differential-algebraic equations with application to particle filtering. In Proceedings of 45th IEEE Conference on Decision and Control, San Diego, CA, USA, 2006. Accepted for
publication.
The main results in Chapter 5 have been published in
M. Gerdin. Local identifiability and observability of nonlinear differentialalgebraic equations. In Proceedings of the 14th IFAC Symposium on System
Identification, Newcastle, Australia, March 2006a,
the results in Chapter 6 have been published in
M. Gerdin and T. Glad. On identifiability of object-oriented models. In Proceedings of the 14th IFAC Symposium on System Identification, Newcastle,
Australia, March 2006,
and the results in Chapter 7 have been published in
M. Gerdin. Using DAE solvers to examine local identifiability for linear and
nonlinear systems. In Proceedings of the 14th IFAC Symposium on System
Identification, Newcastle, Australia, March 2006b.
The main results in Chapter 8 have been developed in cooperation with Dr. Thomas Schön
and Prof. Fredrik Gustafsson and have previously been published in
T. Schön, M. Gerdin, T. Glad, and F. Gustafsson. A modeling and filtering framework for linear differential-algebraic equations. In Proceedings of
the 42nd IEEE Conference on Decision and Control, pages 892–897, Maui,
Hawaii, USA, December 2003.
Part of the results in Chapter 9 and 11 have been submitted as
M. Gerdin, T. B. Schön, T. Glad, F. Gustafsson, and L. Ljung. On parameter
and state estimation for linear differential-algebraic equations. Automatica,
2006. To appear,
and part of the results in Chapter 9 have been published in
M. Gerdin, T. Glad, and L. Ljung. Parameter estimation in linear differentialalgebraic equations. In Proceedings of the 13th IFAC Symposium on System
Identification, pages 1530–1535, Rotterdam, the Netherlands, August 2003.
The results in Chapter 11 have also been published as
M. Gerdin. Computation of a canonical form for linear differential-algebraic
equations. In Proceedings of Reglermöte 2004, Göteborg, Sweden, May
2004.
The results in Chapter 10 have been published as
M. Gerdin, T. Glad, and L. Ljung. Well-posedness of filtering problems for
stochastic linear DAE models. In Proceedings of 44th IEEE Conference on
Decision and Control and European Control Conference ECC 2005, pages
350–355, Seville, Spain, December 2005.
6
1
Introduction
2
Modeling
In this chapter, we will introduce the models and modeling techniques that are discussed
in the thesis. We will also discuss general theory about these models and modeling techniques.
2.1
Introduction: Component-Based Modeling
By modeling, we mean the production of equations that can predict the behavior (or some
part of the behavior) of a system. One way to model a physical system is to write down
all equations describing the physics of a system. For larger systems this is of course a
cumbersome approach. However, if parts of the system have been modeled previously,
the work required can be reduced by reusing these parts. This leads to component-based
modeling.
Component-based modeling is based on the need to combine models of different parts
of a system to form a model of the complete system. The idea is that models of common
parts, or components, are created once and for all, and stored in model libraries. They can
then be reused when modeling larger systems. The components can typically be tuned for
the application at hand by changing the value of parameters.
Consider for example electrical circuits. Typically, the equations describing components like resistors, capacitors, inductors, and voltage sources are stored in model libraries. The parameters of these components are the resistance, capacitance, inductance,
and voltage respectively. When modeling a complete electrical circuit, the included components (including parameter values) and their interconnections are specified.
The modeling process can be simplified further by using modeling software with a
graphical user interface where components can be selected and connected graphically.
This makes it possible for a user to build complex models without having to deal with any
equations. An example of a graphical model of an electrical circuit is shown in Figure 2.1.
7
8
2
Modeling
G
L
C1
C2
Ro
Figure 2.1: A component-based model consisting of one linear resistor, one inductor, two capacitors, one conductor, one nonlinear resistor, and a ground point.
The most wide-spread language for component-based modeling is Modelica (Fritzson, 2004; Tiller, 2001). Modelica is a language for component-based modeling. This
is defined as a programming language specifically designed for component-based modeling. It includes constructs for example for defining equations and components (which can
be connected with other components to create larger models) and possibilities to define
graphical representations for the components. To use Modelica for modeling and simulation, an implementation of the language is necessary. Two implementations are Dymola
and OpenModelica. The commercial product Dymola (Mattsson et al., 1998) is the most
complete implementation available at the time of writing. OpenModelica (Fritzson, 2004,
Chapter 19) is a free implementation with source code available. Modelica is often referred to as a language for object-oriented modeling. This concept includes the principles
of component-based modeling, and also programming specific concepts such as classes
and inheritance.
The main interest in this thesis is to examine how unknown parameters and internal
variables in component-based models can be estimated. To do this, we will need to examine the structure of the equations that component-based modeling results in. This will
be done in the following sections. We will also discuss how stochastic processes can be
used to model disturbances.
2.1.1
Deterministic Models
By deterministic models, we mean models that do not include any stochastic variables or
processes. Models that include stochastic processes or variables will be called stochastic
models and are discussed in Section 2.1.2.
2.1
9
Introduction: Component-Based Modeling
As discussed above, a component-based model consists of a number of components
that each has a number of equations associated with it, and also equations describing how
the components are connected. Each component i is described by equations fi and the
connections are described by equations g. To describe the behavior of the components, internal variables may be necessary. The internal variables of component i will be denoted
li . The internal variables li are not involved in connections with other components. To describe connections with other components external variables wi are used. These variables
are used when describing how the components interact. The differentiation operator with
d
respect to time is denoted p, px(t) = dt
x(t). The components may also contain constant
unknown parameters θi . The collected parameters,
 
θ1
 .. 
θ= . 
(2.1)
θm
are assumed to lie in a set DM ⊆ Rnθ , θ ∈ DM . Furthermore, known external stimuli
on the system are denoted u(t) and measured outputs are denoted y(t).
Example 2.1: Component-based model
Consider the component-based model in Figure 2.1. It consists of seven components:
two resistors, one conductor, two capacitors, one inductor, and one ground point. Let the
inductor be component number 1. The external variables for this component are the potentials at the endpoints, v1 (t) and v2 (t), and the currents at the endpoints, i1 (t) and i2 (t).
The internal variables are the voltage over the inductor uL (t) and the current through the
inductor, iL (t). We thus have


v1 (t)
v2 (t)
 and l1 (t) = uL (t) .
w1 (t) = 
(2.2)
 i1 (t) 
iL (t)
i2 (t)
The equations describing the inductor are


uL (t) − v1 (t) − v2 (t)


i1 (t) − iL (t)

 = 0.


i2 (t) − iL (t)
uL (t) − L · piL (t)
{z
}
|
(2.3)
f1
Let the potential and current at the top endpoint of the resistor R0 be denoted v3 and i3
respectively. The equations
v2 (t) − v3 (t)
=0
(2.4)
i2 (t) − i3 (t)
{z
}
|
part of g
would then be included in g to describe the connection between the inductor and the
resistor R0 .
10
2
Modeling
To summarize, the following parameters, variables, and equations are involved in a
component-based model with m components.
• Internal variables li for each component i = 1, . . . , m.
• External variables wi for each component i = 1, . . . , m. These are grouped as


w1 (t)
 w2 (t) 


w(t) =  .  .
(2.5)
 .. 
wm (t)
• Unknown constant parameters θi for each component i = 1, . . . , m. These are
grouped as


θ1 (t)
 θ2 (t) 


(2.6)
θ(t) =  .  .
 .. 
θm (t)
• Equations
fi li (t), wi (t), θi , p = 0 i = 1, . . . , m,
(2.7)
describing each component. Note that this could be written without the differentiation operator p as
fi li (t), l˙i (t), ¨li (t), . . . , wi (t), ẇi (t), ẅi (t), . . . , θi = 0 i = 1, . . . , m. (2.8)
• Equations
g u(t), w(t) = 0
(2.9)
describing the connections. Some of the wi (t) may be external stimuli, such as the
voltage of a voltage source. This is specified through the known input function u(t)
in g.
• Variables y(t) that are measured, are defined through the equation
y(t) = h w(t), θ
(2.10)
where y(t) is a measured signal. No li are included in this equation, since all signals
that are visible to the outside of the component are included in wi .
Collecting the equations gives the following model.
Model 1: Component-Based Model
fi li (t), wi (t), θi , p = 0
g u(t), w(t) = 0
i = 1, . . . , m
y(t) = h w(t), θ
(2.11a)
(2.11b)
Note that fi , g, li , wi , θi , u, h, and y all may be vector valued.
(2.11c)
2.1
11
Introduction: Component-Based Modeling
Model 1 explicitly shows the structure of the equations that component-based modeling leads to. For example, the variables li are local for each component since only the wi
are involved in the connections. The equations g = 0 are typically simple equations like
a−b = 0 or a+b+c = 0 and each equation normally only includes a small number of the
w. This makes the system sparse, i.e., only a few variables are included in each equation.
This special structure is utilized for example by the solvers for component-based models
included in modeling environments like OpenModelica and Dymola. This is discussed
further in Section 2.5. In Chapter 6 it is discussed how the structure can be used when
examining identifiability of component-based models.
The equations can all be grouped together into one large system of equations to form a
nonlinear differential-algebraic equation (DAE). DAE models are also known as descriptor systems, singular systems, and implicit systems.
Model 2: Nonlinear DAE
F ẋ(t), x(t), θ, u(t) = 0
y(t) = h x(t), θ
(2.12a)
(2.12b)
In comparison with Model 1, the variables l(t) and w(t) have been grouped to form the
vector of internal variables x(t), the unknown parameters have been collected into one
vector θ, and all equations have been included in the DAE F = 0. The equations are also
d
written using only first order derivatives, ẋ(t) = dt
x(t). This is not a limitation, since
higher derivatives can be specified by including additional variables. For example, ẍ(t)
can be replaced by ż(t) by including the equation z(t) = ẋ(t). Nonlinear DAE models
are further discussed in Section 2.2.
In the special case when all equations in Model 2 are linear in x(t), ẋ(t), and u(t), we
get a linear DAE.
Model 3: Linear DAE
E(θ)ẋ(t) = A(θ)x(t) + B(θ)u(t)
y(t) = C(θ)x(t)
(2.13a)
(2.13b)
The reason to separate the linear DAE from its nonlinear counterpart, is that it is easier to
analyze. For example, it is possible to reduce it to state-space form (an ordinary differential equation) explicitly under mild conditions. An analysis of linear DAEs is performed
in Section 2.3.
An important special case of DAE models is when the derivative ẋ(t) can be explicitly
solved from the equations. The equations then form a state-space model,
ẋ(t) = f x(t), θ, u(t)
(2.14a)
y(t) = h x(t), θ .
(2.14b)
12
2
Modeling
If the equations are linear we have a linear state-space model,
ẋ(t) = A(θ)x(t) + B(θ)u(t)
y(t) = C(θ)x(t).
(2.15a)
(2.15b)
Since state-space models are well examined in the literature, the techniques for state-space
models will form the foundation for some of our discussion for DAE systems.
It can be noted that the idea to model a system by writing down all equations describing it, is related to the behavioral approach to modeling discussed by Polderman and
Willems (1998).
2.1.2
Stochastic Models
In practice, mathematical models of physical systems cannot predict the exact behavior
of the system, for example because of disturbances acting on the system. Disturbances
can for example be wind acting on an aircraft or measurement noise in an electrical system. This motivates the introduction of stochastic models where such disturbances are
modeled explicitly as stochastic processes. We will call disturbances that act on a system
noise and we will make a distinction between process noise and measurement noise. Process noise is disturbances that affect the behavior of the system while measurement noise
is disturbances that affect the measurements made on the system. Measurement noise
is often modeled as additive on the measurement, and we will adopt this practice here.
Denoting the process noise with v1 (t) and the measurement noise with v2 (t), a stochastic
component-based model can be written as shown below.
Model 4: Stochastic Component-Based Model
fi li (t), wi (t), θi , p, v1,i (t) = 0
g u(t), w(t) = 0
i = 1, . . . , m
(2.16a)
(2.16b)
y(t) = h w(t), θ + v2 (t)
(2.16c)
The process noise has been divided into m parts, v1,i i = 1, . . . , m to make explicit the
fact that the different components typically are affected by different noise sources. As
with deterministic models, the special structure of the component-based model may not
always be possible to utilize. The equations and variables can then be grouped to form a
nonlinear stochastic differential-algebraic equation (nonlinear SDAE).
Model 5: Nonlinear SDAE
F ẋ(t), x(t), θ, u(t), v1 (t) = 0
(2.17a)
y(t) = h x(t), θ + v2 (t)
If all equations are linear, we get a linear SDAE.
(2.17b)
2.2
Nonlinear DAE Models
13
Model 6: Linear SDAE
E(θ)ẋ(t) = A(θ)x(t) + B(θ)u(t) + K(θ)v1 (t)
y(t) = C(θ)x(t) + v2 (t)
(2.18a)
(2.18b)
Special care must be taken when including stochastic processes in DAE models to make
sure that the variables of interest are well-defined. For nonlinear SDAEs this will be
discussed in Chapter 4, and for linear SDAEs it will be discussed in Chapters 8, 9, and 10.
The properties of stochastic models that will be used in the thesis are discussed in
Section 2.7. As we will see, the stochastic state-space model is an important special case
of stochastic DAE models. In the nonlinear case it can be written as
ẋ(t) = f x(t), θ, u(t) + g x(t), θ, u(t) v1 (t)
(2.19a)
y(t) = h x(t), θ + v2 (t).
(2.19b)
We will limit the discussion
to the case when the noise enters affinely according to the
term g x(t), θ, u(t) v1 (t). We will discuss this further in Section 2.7. In the linear case a
stochastic state-space model can be written as
ẋ(t) = A(θ)x(t) + B(θ)u(t) + K(θ)v1 (t)
(2.20a)
y(t) = C(θ)x(t) + v2 (t).
(2.20b)
These models are called stochastic differential equations (SDEs). As for the deterministic case, the theory for state-space models is well developed, and will be used when
examining SDAE models.
2.2
Nonlinear DAE Models
As discussed above, a (deterministic) differential-algebraic equation (DAE) is a set of
equations that can be written in the form
F ẋ(t), x(t), u(t) = 0.
(2.21)
DAE models are also known as descriptor systems, singular systems, and implicit systems. In this section we will review some of the theory available for such models. Since
properties connected to unknown parameters and measured outputs are not discussed
in this section, we omit these in the notation. Some general references on nonlinear
DAEs are Brenan et al. (1996) that mainly discuss solution techniques and Kunkel and
Mehrmann (2001) that discuss general properties of DAEs. The book by Kunkel and
Mehrmann (2006) is also a good reference.
DAE models are in several ways more difficult to handle than state-space models. The
difficulties center around the fact that it generally is not possible to solve (2.21) for ẋ(t).
If this was possible, the DAE could be written as a state-space system,
ẋ(t) = f x(t), u(t)
(2.22)
14
2
Modeling
so that methods for state-space models could be used. To make it possible to transform a
DAE into a state-space system (or similar description) it is usually necessary to differentiate the equations several times with respect to time. The following example illustrates
this.
Example 2.2: Solving for ẋ(t)
Consider the DAE
ẋ1 (t) + x1 (t)
= 0.
x2 (t) − x21 (t)
{z
}
|
(2.23)
F
It is not possible to directly solve for
ẋ(t) =
ẋ1 (t)
ẋ2 (t)
(2.24)
as a function of x(t). However, if the second equation of (2.23) is differentiated with
respect to time we get
ẋ1 (t) + x1 (t)
= 0.
(2.25)
ẋ2 (t) − 2x1 (t)ẋ1 (t)
which can be solved for ẋ(t) to give
−x1 (t)
−x1 (t)
ẋ1 (t)
=
=
.
2x1 (t)ẋ1 (t)
−2x21 (t)
ẋ2 (t)
(2.26)
The number of times a DAE must be differentiated in order to solve for ẋ(t) is called the
differential index or just the index of the system. The following definition is by Brenan
et al. (1996).
Definition 2.1 (Differential index). The differential index of a DAE,
F ẋ(t), x(t), u(t) = 0,
(2.27)
is the smallest ν such that the system of equations
F ẋ(t), x(t), u(t) = 0
d
F ẋ(t), x(t), u(t) = 0
dt
..
.
ν
d
F ẋ(t), x(t), u(t) = 0
ν
dt
(2.28)
uniquely determines the variable ẋ(t) as a function of x(t), u(t), and derivatives of u(t).
There are also other index concepts defined in the literature, but the differential index
is the most common one. When the term index is used without specification, this typically
refers to the differential index.
It is possible to form DAE systems that do not have a solution. Consider for example
the following DAE.
2.2
15
Nonlinear DAE Models
Example 2.3: DAE without solution
The DAE
ẋ1 (t) + x2 (t) = 0
2
(2.29a)
x3 (t) − t = 0
(2.29b)
x3 (t) + 5 = 0
(2.29c)
does not have any solutions.
We will call those DAE systems where the corresponding initial value problem has at least
one solution solvable DAEs.
Definition 2.2 (Solvable DAE). A DAE is called solvable if the corresponding initial
value problem has at least one solution.
However, even if a DAE is solvable, it will in general not have solutions for all initial
conditions x(0). Consider the following example.
Example 2.4: Consistent initial conditions
The DAE
ẋ1 (t) + x1 (t) = 0
x1 (t) −
x32 (t)
=0
(2.30a)
(2.30b)
has solutions only for initial conditions satisfying x1 (0) − x32 (0) = 0.
Those initial conditions for which the initial value problem has a solution will be called
consistent initial conditions.
Definition 2.3 (Consistent initial conditions). The consistent initial conditions of a
DAE are the initial conditions such that the corresponding initial value problem has at
least one solution.
Kunkel and Mehrmann’s Analysis
One method to examine DAE systems in presented by Kunkel and Mehrmann (2001), and
these results are summarized in this section. For proofs and a complete discussion, the
reader is referred to Kunkel and Mehrmann (2001). A slightly modified presentation of
the results is included in the book by Kunkel and Mehrmann (2006). The results are based
on rank tests and the implicit function theorem, and are therefore only valid locally.
Their most important result for the purpose of this thesis is that, provided a number
of conditions are satisfied, it is possible to view the DAE as a combination of ordinary
differential equations that determine one part of the variables (denoted x1 ) and algebraic
equations determining another part of the variables (denoted x3 ). If some variables are
not determined by the equations, then these are denoted x2 . For a control engineer, the
x2 variables can often be seen as inputs to the system or external stimuli. This division of
the variables is illustrated by the following example.
16
2
Modeling
Example 2.5: Separating the internal variables
Consider the DAE
ẋ1 − x23
= 0.
x3 − x32 − t
{z
}
|
(2.31)
F
This DAE can be written as
ẋ1 = x23
x3 =
x32
(2.32)
+t
(2.33)
where we see that x1 is determined by an ordinary differential equation, x3 is determined
by a static relationship, and x2 can be seen as an external stimulus. Note that it would be
possible to exchange the roles of x2 and x3 in this example. Also note that x1 , x2 , and x3
are scalars in this example, but generally they are vectors.
This example will be used throughout this section to illustrate the concepts discussed.
To simplify notation, we let the DAE depend directly on the time variable t instead of
an input signal u(t),
F ẋ(t), x(t), t = 0
(2.34)
where
F ∈ Rm
x∈R
(2.35a)
n
(2.35b)
t∈I
(2.35c)
where I ⊆ R is a compact interval. Kunkel and Mehrmann’s analysis is based on successive differentiations of the DAE. Therefore define a nonlinear derivative array
Fl (t, x, ẋ, . . . , xl+1 ) = 0
(2.36)
which stacks the original equations and all their derivatives up to level l:



Fl (t, x, ẋ, . . . , xl+1 ) = 

F (ẋ, x, t)

d

dt F (ẋ, x, t) 
..


.
dl
F (ẋ, x, t)
dtl
(2.37)
Example 2.6: Derivative array
For the DAE in Example 2.5 we have
F0 = F =
ẋ1 − x23
x3 − x32 − t
(2.38)
2.2
17
Nonlinear DAE Models
and

ẋ1 − x23
 x3 − x32 − t 

=
 ẍ1 − 2x3 ẋ3  .
ẋ3 − 3x22 ẋ2 − 1

F1 =
F
d
dt F
(2.39)
Partial derivatives of Fl with respect to selected variables p from (t, x, ẋ, . . . , x(l+1) )
are denoted by Fl;p , e.g.,
∂
Fl .
Fl;ẋ,...,x(l+1) = ∂∂ẋ Fl ∂∂ẍ Fl · · · ∂x(l+1)
(2.40)
A corresponding notation is used for partial derivatives of other functions.
Example 2.7: Notation for partial derivatives
For the DAE in example in Example 2.5 we have
0
0
−2x3
F0;x,ẋ =
1
0 −3x22
F0;x =
0
0
0
−3x22
1 0 0
,
0 0 0
−2x3
,
1
(2.41)
(2.42)
and
F0;ẋ =
1
0
0 0
.
0 0
(2.43)
The solution of the derivative array Fµ for some integer µ is denoted
Lµ = {zµ ∈ I × Rn × · · · × Rn |Fµ (zµ ) = 0 } .
(2.44)
Example 2.8: Lµ — solution of the derivative array
For the DAE in example in Example 2.5 we have, with µ = 0,
2
z0,5 − z0,4
3
3
L0 = z0 ∈ I × R × R =0
3
z0,4 − z0,3
− z0,1
(2.45)
where z0,i represents the i:th element of the vector z0 . We thus have z0,1 representing the
time t, z0,2 representing x1 , z0,3 representing x2 , z0,4 representing x3 , z0,5 representing
ẋ1 and so on. The set L0 is shown in Figure 2.2.
18
2
Modeling
5
4
3
0
z0,1
2
2
4
z0,5
1
0
-2
6
8
-1
0
1
10
2
z0,3
Figure 2.2: The set L0 in Example 2.8. For the variables in z0 that are not shown,
3
+ z0,1 and that z0,2 , z0,6 , and z0,7 can take arbitrary values
we have that z0,4 = z0,3
in R.
To present the main results, the corank of a matrix must be defined (Kunkel and
Mehrmann, 2005, page 374).
Definition 2.4 (Corank). The corank of a matrix is the rank deficiency with respect to
rows. The convention that corank F−1;x = 0 is used.
For example, if a matrix has 5 rows and rank 3, the corank is 2. The following property, Hypothesis 1 by Kunkel and Mehrmann (2001), which describes the basic requirements on DAE models handled by the theory can now be formulated.
Property 2.1
Consider the general nonlinear DAE (2.34). There exist integers µ, r, a, d, and v such
that the following conditions hold:
1. The set Lµ ⊆ R(µ+2)n+1 forms a manifold of dimension (µ + 2)n + 1 − r.
2. We have
rank Fµ;x,ẋ,...,x(µ+1) = r
on Lµ .
(2.46)
2.2
19
Nonlinear DAE Models
3. We have
corank Fµ;x,ẋ,...,x(µ+1) − corank Fµ−1;x,ẋ,...,x(µ) = v
(2.47)
rank Fµ;ẋ,...,x(µ+1) = r − a
(2.48)
on Lµ .
4. We have
on Lµ such that there are smooth full rank matrix functions Z2 and T2 defined on
Lµ of size (µ + 1)m, a and (n, n − a), respectively, satisfying
Z2T Fµ;ẋ,...,x(µ+1) = 0
(2.49a)
rank Z2T Fµ;x
Z2T Fµ;x T2
=a
(2.49b)
=0
(2.49c)
on Lµ .
5. We have
rank Fẋ T2 = d = m − a − v
(2.50)
on Lµ .
One of the more restrictive assumptions of Property 2.1 is that all ranks are constant. This
for example rules out models where the number of states changes over time. Apart from
that, the assumptions of Property 2.1 are not very restrictive and are satisfied for many
physical models.
The property makes it possible to define a new index concept, the strangeness index
(Kunkel and Mehrmann, 2001, 2006).
Definition 2.5 (Strangeness index). The strangeness index of the DAE system (2.34) is
the smallest µ such that Property 2.1 is satisfied.
Example 2.9: Verifying Property 2.1
In this example, we show that the system in Example 2.5 fulfills Property 2.1 with µ = 0,
and thus has strangeness index 0. Note that the dimension of x is n = 3 and the dimension
of F is m = 2. Take µ = 0. The parts of Property 2.1 can now be verified as follows.
1. From (2.45) and Figure 2.2 we get that L0 forms a manifold of dimension 5. We
must thus have
r = (µ + 2)n + 1 − 5 = 2.
(2.51)
2. From (2.41) we get that
rank F0;x,ẋ = 2
which is consistent with r = 2.
3. F0;x,ẋ has full row rank, so v = 0.
(2.52)
20
2
Modeling
4. From (2.43) we get that
rank F0,ẋ = 1
(2.53)
a = r − 1 = 1.
(2.54)
which gives
The matrices Z2 and T2 can be taken as
0
Z2 =
1
and

1
T 2 = 0
0
(2.55)

0
1 .
3x22
(2.56)
5. We have
1
rank Fẋ T2 = rank
0

1
0 
0
0
0
0
0

0
1

1
= rank
0
3x22
0
0
= 1.
(2.57)
This is consistent with
m − a − v = 2 − 1 − 0 = 1,
(2.58)
so we have a well-defined d = 1.
For DAE models that satisfy the property it is, as mentioned in the beginning of the
section, possible to divide the equations into one part that (locally) forms an ordinary
differential equation for one part of the variables (denoted x1 ), and one part that locally
forms static equations that determine another part of the variables (denoted x3 ). If any
variables still are undetermined, then they can be chosen freely. These variables are denoted x2 . Note that no variable transformation is necessary, only a reordering of the
variables needs to be done. This means that we can write


x1 (t)
(2.59)
x(t) = Q x2 (t) , Q permutation matrix.
x3 (t)
This transformation is performed by first defining the m × d matrix Z1 (which can be
chosen constant) such that
rank Z1T Fẋ T2 = d
(2.60)
and then forming
F̂1 = Z1T F
(2.61a)
F̂2 = Z2T Fµ .
(2.61b)
2.2
21
Nonlinear DAE Models
This gives the equations
F̂1 (t, x1 , x2 , x3 , ẋ1 , ẋ2 , ẋ3 ) = 0
(2.62a)
F̂2 (t, x1 , x2 , x3 ) = 0.
(2.62b)
Example 2.10: Computing F̂1 and F̂2
For the DAE in example 2.5 we can take
1
Z1 =
0
(2.63)
and we thus get
ẋ1 − x23
= ẋ1 − x23
F̂1 = 1 0
3
| {z } x3 − x2 − t
{z
}
|
Z1T
F
ẋ1 − x23
F̂2 = 0 1
= x3 − x32 − t.
3
| {z } x3 − x2 − t
|
{z
}
ZT
2
(2.64a)
(2.64b)
F0
F̂2 = 0 can (locally) be solved for x3 to give the equations
F̂1 (t, x1 , x2 , x3 , ẋ1 , ẋ2 , ẋ3 ) = 0
x3 = R(t, x1 , x2 ).
(2.65a)
(2.65b)
After using (2.65b) to eliminate x3 and ẋ3 in (2.65a), (2.65a) can be locally solved for ẋ1
to give
ẋ1 = L(t, x1 , x2 , ẋ2 )
(2.66a)
x3 = R(t, x1 , x2 ).
(2.66b)
Example 2.11: Separating the internal variables
For the DAE in Example 2.5 we can solve F̂1 = 0 and F̂2 = 0 for ẋ1 and x3 :
ẋ1 = x23
x3 =
(2.67a)
x32
+ t.
(2.67b)
Eliminating x3 in (2.67a) using (2.67b) gives
ẋ1 = (x32 + t)2
| {z }
(2.68a)
x3 = x32 + t .
| {z }
(2.68b)
L
R
22
2
Modeling
The above discussion can be summarized by the following theorem, which is a version
of Theorem 3 by Kunkel and Mehrmann (2001).
Theorem 2.1
Let F in (2.34) satisfy Property 2.1 with µ, a, d, v. Then every solution of (2.34) solves a
reduced problem,
ẋ1 = L(t, x1 , x2 , ẋ2 )
(2.69a)
x3 = R(t, x1 , x2 )
(2.69b)
consisting of d differential and a algebraic equations. The elements of x1 ∈ Rd , x2 ∈
Rn−a−d , and x3 ∈ Ra together make up the elements of x.
Proof: See Theorem 3 in Kunkel and Mehrmann (2001).
Note that it is typically not possible to solve for ẋ1 and x3 explicitly (the existence of
the transition is proved using the implicit function theorem). Instead it is usually necessary
to work with F̂1 and F̂2 and solve for ẋ1 and x3 numerically. However, it is possible to
solve explicitly for ẋ3 . This can be seen by differentiating (2.62b) with respect to time,
d
F̂2 = F̂2;t + F̂2;x1 ẋ1 + F̂2;x2 ẋ2 + F̂2;x3 ẋ3 .
dt
(2.70)
Since F̂2 can be solved locally for x3 , F̂2;x3 is non-singular. This means that ẋ3 can be
written as
−1
ẋ3 = −F̂2;x
F̂
+
F̂
ẋ
+
F̂
ẋ
(2.71)
2;t
2;x
1
2;x
2
1
2
3
−1
where F̂2;x
is the inverse of the matrix F̂2;x3 . We can thus expect to work with equations
3
like
F̃1 (t, x1 , x2 , x3 , ẋ1 , ẋ2 ) = 0
(2.72a)
F̂2 (t, x1 , x2 , x3 ) = 0
(2.72b)
where F̃1 is F̂1 with ẋ3 eliminated using (2.71).
The theorem above states that every solution of the DAE also solves the reduced system. To show that the solutions of the reduced systems solve the original DAE, additional
requirements must be fulfilled as stated by the following theorem. In this theorem, Property 2.1 must be satisfied for two successive values of µ with the other constants in the
property unchanged.
Theorem 2.2
Let F in (2.34) be sufficiently smooth and satisfy Property 2.1 with µ, a, d, v and with
0
µ + 1 (replacing µ), a, d, v. Let the initial condition zµ+1
∈ Lµ+1 be given and let
the parameterization of the solution of Fµ+1 include ẋ2 . Then, for every function x2 ∈
0
C 1 · (I, Rn−a−d ) that satisfies zµ+1
, the reduced system
ẋ1 = L(t, x1 , x2 , ẋ2 )
(2.73a)
x3 = R(t, x1 , x2 )
(2.73b)
0
has unique solutions x1 and x3 where x1 satisfies the initial condition zµ+1
. Moreover,
these together locally solve the original problem.
2.2
23
Nonlinear DAE Models
Proof: See Theorem 4 in Kunkel and Mehrmann (2001).
The term “locally solves” in this theorem refers to the fact that solutions to (2.73) only
represents one set of solutions to the original DAE. There could possibly be solutions with
√
other properties. For example, the equation x = y “locally solves” x2 −y = 0 for y > 0.
It is possible to select the initial value x1 (0) freely in a neighborhood of each possible
value as noted by the following proposition.
Proposition 2.1
0
Let F in (2.34) satisfy the conditions of Theorem 2.2. Let x10 be the part of zµ+1
∈ Lµ+1
0
belonging to x1 . If x̃10 is sufficiently close to x10 , it is part of a z̃µ+1
∈ Lµ+1 close to
0
0
0
zµ+1
and Theorem 2.2 can be applied with zµ+1
replaced by z̃µ+1
. The same holds for
x2 .
Proof: It follows from the proof of Theorem 4 in Kunkel and Mehrmann (2001) that
Lµ+1 locally can be parameterized by t, x1 , x2 , and p where p is chosen from ẋ . . . xµ+2 .
x̃10 can thus be chosen freely if it is sufficiently close to x10 . The same holds for x2 .
If there are no free parameters x2 , Theorem 2.2 simplifies to the following corollary.
Corollary 2.1
Let F in (2.34) be sufficiently smooth and satisfy Property 2.1 with µ, a, d, v and with
0
∈ Lµ+1 the
µ + 1 (replacing µ), a, d, v, and assume that a + d = n. Fore every zµ+1
reduced problem
ẋ1 = L(t, x1 )
(2.74a)
x3 = R(t, x1 )
(2.74b)
0
has a unique solution satisfying the initial value given by zµ+1
. Moreover, this solution
locally solves the original problem.
Proof: See Corollary 5 in Kunkel and Mehrmann (2001).
The forms discussed here will be important tools when examining noise modeling,
identifiability, and observability for nonlinear DAE models. The transformation is typically not unique, for example there may be different possible choices of state variables x1 .
It is also common that the DAE is well determined so that x2 has size zero, as for example
in Corollary 2.1. This is defined as regularity.
Definition 2.6 (Regularity). The DAE
F ẋ(t), x(t), u(t) = 0
(2.75)
is called regular if it satisfies Property 2.1 and n − a − d = 0 or, equivalently, the size of
x2 is equal to zero.
When using the method discussed in this section, it is usually necessary to successively increase µ until the property is true. The property could for example be verified
by numeric rank tests at a certain value of x(t), see further Remark 1 by Kunkel and
Mehrmann (2001). The practical implementation of methods related to the property is
also discussed in a more recent paper by Kunkel and Mehrmann (2004).
24
2.3
2
Modeling
Linear DAE Models
In this section we will discuss some concepts concerning linear DAE systems that will be
needed to motivate or develop the theory discussed in later chapters. The theory for linear
DAE systems is presented separately since the linear structure allows a detailed analysis
than for the nonlinear case. Linear DAEs are also known as linear descriptor systems,
linear singular systems, and linear implicit systems.
2.3.1
Introduction
A linear DAE is a system of equations in the form
E ẋ(t) = Jx(t) + Ku(t)
y(t) = Lx(t).
(2.76a)
(2.76b)
In this description E and J are constant square matrices and K and L are constant matrices. Note that E may be a singular matrix. This makes it possible to include a purely
algebraic equation in the description for example by letting a row of E be equal to zero.
The vectors u(t) and y(t) are the input and the measured output respectively. Finally, the
vector x(t) contains the internal variables that describe the current state of the system.
It is also possible to form a discrete-time counterpart of the linear DAE (2.76).
Ex(t + 1) = Jx(t) + Ku(t)
y(t) = Lx(t)
(2.77a)
(2.77b)
This model is called a system of difference-algebraic equations or a discrete-time descriptor system.
Two references on linear DAE systems are the book by Dai (1989b) and the survey
by Lewis (1986). They discuss both general properties of linear DAE systems such as
regularity and canonical forms, as well as controllability, observability, and different control and estimation strategies. They are both focused on the continuous-time case, but also
treat discrete-time systems. Many references to earlier work are provided by both authors.
Within the numerical analysis literature, Brenan et al. (1996) is worth mentioning. The
main topic is the numerical solution of nonlinear DAEs, but linear DAE systems are also
treated. One can also note that linear DAE systems are special cases of the general linear
constant differential equations discussed by Rosenbrock (1970). Rosenbrock’s analysis
is mainly carried out in the frequency domain. Linear DAE systems are also special cases
of the general differential systems discussed by Kailath (1980, Chapter 8). Linear DAE
models are also discussed in the book by Kunkel and Mehrmann (2006).
The main topics of this section is to describe how the linear DAE system (2.76) can
be transformed into different canonical forms (Section 2.3.3), and how it can be further
transformed into a state-space system with a redefined input (Section 2.3.5). It is also
discussed how a linear DAE system can be sampled by first transforming it to state-space
form in Section 2.3.6. In Section 2.6 the results for the continuous-time case are extended
to the discrete-time case.
Before proceeding into details of the canonical forms it may be worthwhile to note
that (2.76) has the transfer function
G(s) = L(sE − J)−1 K.
(2.78)
2.3
25
Linear DAE Models
A difference between G(s) in (2.78) and the transfer function of a state-space system is
that G(s) in (2.78) may be non-proper (have higher degree in the numerator than in the
denominator) in the general case. This can be realized from the following example:

−1

 1 0 
 0 1
−
s

0 1 
 0 0
| {z } | {z }
E
1
=−
0
s
1
(2.79)
J
It can be noted that the transfer function in (2.78) only is well-defined if (sE − J)
is non-singular. In Section 2.3.2 we will define the non-singularity of this matrix as regularity of the system (2.76). We will also see that regularity of a linear DAE system is
equivalent to the existence of a unique solution.
2.3.2
Regularity
A basic assumption which is made throughout this thesis is that the inverse in (2.78) is
well-defined, and therefore we formalize this with a definition.
Definition 2.7 (Regularity). The linear DAE system
E ẋ(t) = Jx(t) + Ku(t)
y(t) = Lx(t)
(2.80a)
(2.80b)
det(sE − J) 6≡ 0,
(2.81)
is called regular if
that is the determinant is not zero for all s.
This definition is the same as the one used by Dai (1989b). The reason that regularity
of a linear DAE system is a reasonable assumption, is that it is equivalent to the existence of a unique solution, as discussed by Dai (1989b, Chapter 1). To illustrate this, we
examine the Laplace transformed version of (2.80a):
sEL [x(t)] − Ex(0) = JL [x(t)] + KL [u(t)]
(2.82)
where L[·] means the Laplace transform of the argument. Rearranging this, we get
(sE − J) L [x(t)] = KL [u(t)] + Ex(0)
If the system is regular, we get that L [x(t)] is uniquely determined by
−1
L [x(t)] = (sE − J)
KL [u(t)] + Ex(0) .
(2.83)
(2.84)
If, on the other hand, the system is not regular, there exists a vector α(s) 6≡ 0 such
that
(sE − J)α(s) ≡ 0.
(2.85)
We get that if the system is not regular and a solution of (2.83) is L [x(t)], then so is
L [x(t)] + kα(s) for any constant k. A solution is consequently not unique. It is also
26
2
Modeling
obvious that a solution may not even exist if the system is not regular, for example if
(sE − J) ≡ 0.
To draw conclusions about x(t) from the existence of L [x(t)], we should examine if
the inverse Laplace transform exists. We do not go into these technicalities here. However,
in the next section we will see how a regular linear DAE system can be transformed into
a form where the existence of a solution is obvious.
It is usually a reasonable assumption that a system has an input which uniquely determines the value of the internal variables for each consistent initial condition. With this
motivation, it will be assumed throughout this thesis that the systems encountered are
regular.
We conclude this section with a small example to illustrate the connection between
solvability and regularity.
Example 2.12: Regularity
x2 (t)
F (t)
m
x1 (t)
Figure 2.3: A body affected by a force.
Consider the body with mass m in Figure 2.3. The body has position x1 (t) and velocity x2 (t) and is affected by a force F (t). The equations describing the system are
ẋ1 (t) = x2 (t)
(2.86a)
mẋ2 (t) = F (t)
(2.86b)
which also can be written as
1 0
ẋ1 (t)
0 1
x1 (t)
0
=
+
F (t)
0 m
ẋ2 (t)
0 0
x2 (t)
1
| {z }
| {z }
|{z}
E
J
(2.87)
K
which is a linear DAE system (without output equation). We get that
det(sE − J) = ms2
(2.88)
and the system is regular if and only if m 6= 0. According to the discussion earlier this
gives that there exists a unique solution if and only if m 6= 0. This is also obvious from
the original equations (2.86). In this example we also see that regularity is a reasonable
requirement on the system.
2.3
27
Linear DAE Models
2.3.3
A Canonical Form
In this section we examine how a linear DAE system can be rewritten in a form which
resembles a state-space system and explicitly shows how the solution of the DAE system
can be obtained. This transformation will later play an important role in the development
of the identification algorithms. Similar transformations have been considered earlier in
the literature (see e.g., Dai, 1989b), but the proofs which are presented in this section have
been constructed so that the indicated calculations can be computed by numerical software
in a reliable manner. How the different steps of the proofs can be computed numerically
is studied in detail in Chapter 11. It can be noted that the system must be regular for the
transformation to exist, but as discussed in Section 2.3.2 regularity is equivalent to the
existence of a unique solution.
The main result is presented in Theorem 2.3, but to derive this result we use a series
of lemmas as described below. The first lemma describes how the system matrices E and
J simultaneously can be written in triangular form with the zero diagonal elements of E
sorted to the lower right block.
Lemma 2.1
Consider a system
E ẋ(t) = Jx(t) + Ku(t)
y(t) = Lx(t).
(2.89a)
(2.89b)
If (2.89) is regular, then there exist non-singular matrices P1 and Q1 such that
J1 J2
E1 E2
P1 EQ1 =
and P1 JQ1 =
0 J3
0 E3
(2.90)
where E1 is non-singular, E3 is upper triangular with all diagonal elements zero and J3
is non-singular and upper triangular.
Note that either the first or the second block row in (2.90) may be of size zero.
Proof: The Kronecker canonical form of a regular matrix pencil which is discussed by,
e.g., Kailath (1980, Chapter 6) immediately shows that it is possible to perform the transformation (2.90).
In the case when the matrix pencil is regular, the Kronecker canonical form is also
called the Weierstrass canonical form. The Kronecker and Weierstrass canonical forms
are also discussed by (Gantmacher, 1960, Chapter 12). The original works are by Weierstrass (1868) and Kronecker (1890).
Note that the full Kronecker form is not computed by the numerical software discussed
in Chapter 11. The Kronecker form is here just a convenient way of showing that the
transformation (2.90) is possible.
The next two lemmas describe how the internal variables of the system can be separated into two parts by making the system matrices block diagonal.
28
2
Lemma 2.2
Consider (2.90). There exist matrices L and R such that
I L
E1 E2
I R
E1
=
0 I
0 E3
0 I
0
and
L
J1
I
0
I
0
J2
J3
I
0
R
I
=
J1
0
0
E3
Modeling
0
.
J3
(2.91)
(2.92)
Proof: See Kågström (1994) and references therein for a proof of this lemma.
Lemma 2.3
Consider a system
E ẋ(t) = Jx(t) + Ku(t)
y(t) = Lx(t).
(2.93a)
(2.93b)
If (2.93) is regular, there exist non-singular matrices P and Q such that the transformation
P EQQ−1 ẋ(t) = P JQQ−1 x(t) + P Ku(t)
(2.94)
gives the system
I
0
0
N
Q−1 ẋ(t) =
0
B
Q−1 x(t) +
u(t)
I
D
A
0
(2.95)
where N is a nilpotent matrix.
Proof: Let P1 and Q1 be the matrices in Lemma 2.1 and define
I L
P2 =
0 I
I R
Q2 =
0 I
−1
E1
0
P3 =
0
J3−1
(2.96a)
(2.96b)
(2.96c)
where L and R are from Lemma 2.2. Also let
P = P3 P2 P 1
(2.97a)
Q = Q1 Q2 .
(2.97b)
Then
P EQ =
and
P JQ =
I
0
0
J3−1 E3
E1−1 J1
0
0
I
(2.98)
(2.99)
Here N = J3−1 E3 is nilpotent since E3 is upper triangular with zero diagonal elements
and J3−1 is upper triangular. J3−1 is upper triangular since J3 is. Defining A = E1−1 J1
finally gives us the desired form (2.95).
2.3
29
Linear DAE Models
We are now ready to present the main result in this section, which shows how the
solution of linear DAEs can be obtained. We get this result by observing that the first
block row of (2.95) is a normal state-space description and showing that the solution of
the second block row is a sum of the input and some of its derivatives.
Theorem 2.3
Consider a system
E ẋ(t) = Jx(t) + Ku(t)
y(t) = Lx(t).
(2.100a)
(2.100b)
If (2.100) is regular, its solution can be described by
ẋ1 (t) = Ax1 (t) + Bu(t)
x2 (t) = −Du(t) −
m−1
X
N i Du(i) (t)
(2.101a)
(2.101b)
i=1
x1 (t)
= Q−1 x(t)
x2 (t)
x (t)
.
y(t) = LQ 1
x2 (t)
(2.101c)
(2.101d)
Proof: According to Lemma 2.3 we can without loss of generality assume that the system
is in the form
ẋ1 (t)
A 0
x1 (t)
B
I 0
=
+
u(t)
(2.102a)
ẋ2 (t)
0 I
x2 (t)
D
0 N
x1 (t)
= Q−1 x(t)
(2.102b)
x2 (t)
x (t)
(2.102c)
y(t) = LQ 1
x2 (t)
where
x1 (t)
x2 (t)
(2.103)
is partitioned according to the matrices.
Now, if N = 0 we have that
x2 (t) = −Du(t)
(2.104)
and we are done. If N 6= 0 we can multiply the second block row of (2.102a) with N to
get
N 2 ẋ2 (t) = N x2 (t) + N Du(t).
(2.105)
We now differentiate (2.105) and insert the second block row of (2.102a). This gives
x2 (t) = −Du(t) − N Du̇(t) + N 2 ẍ2 (t)
(2.106)
30
2
Modeling
If N 2 = 0 we are done, otherwise we just continue until N m = 0 (this is true for some
m since N is nilpotent). We would then arrive at an expression like
x2 (t) = −Du(t) −
m−1
X
N i Du(i) (t)
(2.107)
i=1
and the proof is complete.
Note that the internal variables of the system, and therefore also the output, may
depend directly on derivatives of the input. In the case of no dependence on the derivative
of the input, we will have
N D = 0.
(2.108)
This relation will also play an important role in Chapter 8 where it is examined how
noise can be added to the system without having to accept derivatives of the noise in the
solution.
We conclude the section with an example which shows what the form (2.101) is for a
simple electrical system.
Example 2.13: Canonical form
Consider the electrical circuit in Figure 2.4. With I1 (t) as the output and u(t) as the input,
I1 (t)
I2 (t)
u(t)
R
I3 (t)
L
Figure 2.4: A small electrical circuit.
the equations describing the systems are

0
0
0
0
0
0

 
I˙1 (t)
L
0
0  I˙2 (t) = 1
0
0
I˙3 (t)
y(t) = 1

  
0
I1 (t)
1
−1 I2 (t) + 0 u(t)
0
I3 (t)
1


I1 (t)
0 I2 (t) .
I3 (t)
0
−1
−R
0
(2.109a)
(2.109b)
2.3
31
Linear DAE Models
Transforming the system into the form (2.95) gives

1
0
0
0
0
0

0
0
0  0
0
1


I˙1 (t)
0 1
1 0  I˙2 (t) =
0 −1
I˙3 (t)


0 0 0
0 0
 0 1 0  0 1
0 0 1
1 0

  1 
1
I1 (t)
L
0  I2 (t) + − R1  u(t) (2.110a)
−1
I3 (t)
− R1

y(t) = 1
0

I1 (t)
0 I2 (t) .
I3 (t)
(2.110b)
Further transformation into the form (2.101) gives
1
u(t)
L
− R1
x2 (t) = −
u(t)
− R1



0 0 1
I1 (t)
x1 (t)
= 0 1 0  I2 (t)
x2 (t)
1 0 −1
I3 (t)


1 0 1
x (t)
y(t) = 1 0 0 0 1 0 1
.
x2 (t)
1 0 0
ẋ1 (t) =
(2.111a)
(2.111b)
(2.111c)
(2.111d)
We can here see how the state-space part has been singled out by the transformation.
In (2.111c) we can see that the state-space variable x1 (t) is equal to I3 (t). This is natural,
since the only dynamic element in the circuit is the inductor. The two variables in x2 (t)
are I2 (t) and I1 (t) − I3 (t). These variables depend directly on the input.
2.3.4
Alternative Canonical Forms
The transformations presented above are the ones that will be used in this thesis, mainly
because they clearly show the structure of the system and because they can be computed
with numerical software as will be discussed in Chapter 11. Several other transformations
have been suggested in the literature, so we will review some alternative transformations
here. All methods discussed assume that the linear DAE system is regular.
Shuffle Algorithm
The shuffle algorithm, which was suggested by Luenberger (1978), was as the name suggests presented as an algorithm to reach a certain canonical form. The non-reduced form
32
2
Modeling
of the shuffle algorithm applied to the DAE system (2.76) gives the canonical form
!
m
X
(i)
−1
¯
K̄i u (t) .
(2.112)
Jx(t) +
ẋ(t) = Ē
i=0
¯ and K̄i . The shuffle algorithm has the
We show below how to calculate the matrices Ē, J,
advantage that no coordinate transformation is necessary. However, in (2.112) it looks as
if the initial condition x(0) can be chosen arbitrarily, which normally is not the case. It is
instead partly determined by u(0) and its derivatives. There is also a reduced form of the
shuffle algorithm which explicitly shows how the initial conditions can be chosen.
The form (2.112) is computed by first transforming the matrix
E J K
(2.113)
by row operations (for example Gauss elimination) into the form
E1 J1 K1
0 J2 K2
where E1 has full row rank. We now have the system
K1
E1
J1
u(t).
x(t) +
ẋ(t) =
K2
0
J2
By differentiating the second row (this is the “shuffle” step) we get
J1
K1
0
E1
ẋ(t) =
x(t) +
u(t) +
u̇(t).
0
0
K2
−J2
| {z }
| {z }
| {z }
| {z }
Ē
J¯
K̄0
(2.114)
(2.115)
(2.116)
K̄1
Note that we through this differentiation loose information about the connection between
the initial conditions x(0) and u(0). If Ē is non-singular, we just multiply by Ē −1 from
the left to get (2.112). If it is singular, the process is continued until we get a nonsingular Ē.
SVD Coordinate System
The SVD coordinate system of the DAE system (2.76) is calculated by taking the singular
value decomposition (SVD) of E,
Σ 0
T
U EV =
(2.117)
0 0
where Σ contains the non-zero singular values of E and U and V are orthogonal matrices.
The transformation
U EV T V −T ẋ(t) = U JV T V −T x(t) + U Ku(t)
(2.118)
2.3
33
Linear DAE Models
then gives the system
Σ 0
J11
V −T ẋ(t) =
0 0
J21
J12
K1
V −T x(t) +
u(t).
J22
K2
(2.119)
Here, V −T is the inverse of V T . Note that V −T = V since V is an orthogonal matrix.
It can be noted that the block rows here do not need to have the same size as the
block rows in the canonical form (2.95). The SVD coordinate system was discussed by
Bender and Laub (1987) who use it to examine general system properties and to derive
a linear-quadratic regulator for linear DAE systems. This transformation cannot immediately be used to get a state-space-like description, but it is used as a first step in other
transformations (e.g., Kunkel and Mehrmann, 1994).
Triangular Form
We get the triangular form if we stay with the description in Lemma 2.1. The transformed
system is then
K1
J1 J2
E1 E2
−1
−1
u(t)
(2.120)
Q1 x(t) +
Q1 ẋ(t) =
K2
0 J3
0 E3
where E1 is non-singular, E3 is upper triangular with all diagonal elements zero and J3 is
non-singular and upper triangular. Using this form we could derive an expression similar
to (2.101). A drawback is that here both x1 (t) and x2 (t) would depend on derivatives
of u(t), which can be verified by making calculations similar to those in the proof of
Theorem 2.3. A good thing about this form is that the matrices L and R of Lemma 2.2 do
not have to be computed.
2.3.5
State-Space Form
Within the control community, the theory for state-space systems is much more developed than the theory for DAE systems. For state-space systems there are many methods
available for control design, state estimation and system identification, see e.g., Glad and
Ljung (2000), Kailath et al. (2000), and Ljung (1999). For linear state-space systems it is
also well established how the systems can be sampled, that is how an exact discrete-time
counterpart of the systems can be calculated under certain assumptions on the input (e.g.,
Åström and Wittenmark, 1984). To be able to use these results for linear DAE systems,
we in this section examine how a linear DAE system can be transformed into a linear
state-space system. We will see that a linear DAE system always can be transformed to a
state-space system if we are allowed to redefine the input as one of its derivatives.
What we will do is to transform a linear DAE system
E ẋ(t) = Jx(t) + Ku(t)
y(t) = Lx(t)
(2.121a)
(2.121b)
into state-space form,
ż(t) = Az(t) + B ũ(t)
(2.122a)
y(t) = Cz(t) + ũ(t).
(2.122b)
34
2
Modeling
Here we have written ũ(t) in the state-space form to point out the fact that the input might
have to be redefined as one of its derivatives. We will assume that the DAE system is
regular. This implies, according to Theorem 2.3, that the system can be transformed into
the form
ẋ1 (t) = Ax1 (t) + Bu(t)
x2 (t) = −Du(t) −
m−1
X
(2.123a)
N i Du(i) (t)
(2.123b)
i=1
x1 (t)
= Q−1 x(t)
x2 (t)
x1 (t)
.
y(t) = LQ
x2 (t)
(2.123c)
(2.123d)
If m = 1 no derivatives of u(t) occur in the description and we directly get that (2.123)
is equivalent to the state-space description
ẋ1 (t) = |{z}
A x1 (t) + |{z}
B u(t)
Ã
(2.124a)
B̃
I
0
y(t) = LQ
x1 (t) + LQ
u(t).
0
−D
| {z }
| {z }
C̃
(2.124b)
D̃
If m > 1, the idea is to redefine the input as its m−1:th derivative, so the original input
and some of its derivatives need to be included as state variables in the new description.
We therefore define a vector with the input and some of its derivatives,



x3 (t) = 

u(t)
u̇(t)
..
.



.

(m−2)
u
(t)
(2.125)
This vector will be part of the state vector in the transformed system. To be able to include
x3 (t) in the state vector, we need to calculate its derivative with respect to time:

u̇(t)
ü(t)
..
.


0

  ..

 .
ẋ3 (t) = 
=

 0
(m−1)
0
u
(t)
I
..
.
0
0
...
..
.
...
...

 
0
0
 .. 
.. 
 
.
 x3 (t) +  .  u(m−1) (t)

0
I
0
I
(2.126)
We can now rewrite (2.123) to depend on x3 (t) instead of depending directly on the
2.3
35
Linear DAE Models
different derivatives of u(t). The new description will be
ẋ1 (t) = Ax1 (t) + B 0 . . . 0 x3 (t)
x2 (t) = − D N D . . . N m−2 D x3 (t) − N m−1 Du(m−1) (t)


 
0 I ... 0
0
 .. .. . .
 .. 
.. 

 
. .
ẋ3 (t) =  . .
 x3 (t) +  .  u(m−1) (t)
0 0 . . . I 
0
0 0 ... 0
I
x (t)
y(t) = LQ 1
x2 (t)
(2.127a)
(2.127b)
(2.127c)
(2.127d)
The final step to obtain a state-space description is to eliminate x2 (t) from these equations.
The elimination is performed by inserting (2.127b) into (2.127d):
 


0
A B 0 ... 0
0

0
0
I
.
.
.
0

 


ẋ1 (t)

.. .. . .
..  x1 (t) +  ..  u(m−1) (t)
(2.128a)
=  ...
.

.
.
.
.
ẋ3 (t)
 

 x3 (t)
0
 0 0 0 . . . I
I
0 0 0 ... 0
| {z }
{z
}
|
Ã
I
y(t) = LQ
0
|
B̃
0
−D
x1 (t)
0
...
0
x3 (t)
−N D . . . −N m−2 D
{z
}
C̃
0
+ LQ
u
−N m−1 D
{z
}
|
(2.128b)
(m−1)
(t)
D̃
If we let
z(t) =
x1 (t)
x3 (t)
(2.129)
this can be written in the compact form
ż(t) = Ãz(t) + B̃u(m−1) (t)
y(t) = C̃z(t) + D̃u
(m−1)
(t).
(2.130a)
(2.130b)
The main purpose of this thesis is to examine how unknown parameters and internal
variables in DAE systems can be estimated, and this is what the state-space system will
be used for in the following. However, as pointed out in the beginning, it may be useful
to do the conversion in other cases as well, for example when designing controllers. The
controller would then generate the control signal u(m−1) (t). In order to obtain the actual
control signal u(t) we have to integrate u(m−1) (t). For a further discussion on this, see
e.g., the paper by Müller (2000).
We conclude the section by continuing Example 2.13 and writing the system in statespace form.
36
2
Modeling
Example 2.14: State-space form
In Example 2.13 we saw that the equations for the electrical circuit could be written as
1
u(t)
L
− R1
x2 (t) = −
u(t)
− R1



0 0 1
I1 (t)
x1 (t)
= 0 1 0  I2 (t)
x2 (t)
1 0 −1
I3 (t)


1 0 1
x1 (t)


0 1 0
y(t) = 1 0 0
.
x2 (t)
1 0 0
ẋ1 (t) =
(2.131a)
(2.131b)
(2.131c)
(2.131d)
Since m = 1 (no derivatives of u(t) occur in the description), x3 (t) is not necessary
and (2.124) can be used. This gives us the state-space description
ż(t) =
1
u(t)
L
(2.132a)
1
u(t).
(2.132b)
R
For this simple case, the state-space description could have been derived manually from
the original equations, but the procedure in the example shows how we can compute the
state-space description automatically. For larger systems it may be more difficult to derive
the state-space description manually.
y(t) = z(t) +
2.3.6
Sampling
As discussed earlier, the theory for state-space systems is much more developed than the
theory for DAE systems. In the previous section, we showed how a linear DAE system can
be transformed into a continuous-time state-space system, which gives us the possibility
to use theory for continuous-time state-space systems. However, in many cases measured
data from a system is available as sampled data. This could be the case both for control,
for estimation, and for system identification. To handle such cases for continuous-time
state-space systems, one common approach is to sample the state-space system, that is to
calculate a discrete-time counterpart of the state-space system. In this section we examine
how a linear DAE system can be sampled.
The basic result for sampling of state-space systems with piecewise constant input is
given in Lemma 2.4 below. The main result of this section is the extension of this lemma
to linear DAE systems.
Lemma 2.4
Consider the state-space system
ż(t) = Az(t) + Bu(t)
(2.133a)
y(t) = Cz(t) + Du(t).
(2.133b)
2.3
37
Linear DAE Models
If u(t) is constant for Ts k ≤ t < Ts k + Ts for constant Ts and k = 0, 1, 2, ..., then
z(Ts k) and y(Ts k) are exactly described by the discrete-time state-space system
z(Ts k + Ts ) = Φz(Ts k) + Γu(Ts k)
y(Ts k) = Cz(Ts k) + Du(Ts k),
(2.134a)
(2.134b)
where
Φ = eATs
ZTs
Γ=
(2.135)
eAτ dτ B.
(2.136)
0
Proof: See, for example, the book by Åström and Wittenmark (1984).
Now, if we assume that u(m−1) (t) is piecewise constant, Lemma 2.4 can be applied
to (2.124) or (2.130) to give an exact discrete-time description of the original linear DAE
system. We have thus arrived at the following theorem:
Theorem 2.4
Consider the regular linear DAE system
E ẋ(t) = Jx(t) + Ku(t)
(2.137a)
y(t) = Lx(t)
(2.137b)
with the canonical form
ẋ1 (t) = Ax1 (t) + Bu(t)
x2 (t) = −Du(t) −
m−1
X
(2.138a)
N i Du(i) (t)
(2.138b)
i=1
x1 (t)
= Q−1 x(t)
x2 (t)
x1 (t)
y(t) = LQ
.
x2 (t)
(2.138c)
(2.138d)
If u(m−1) (t) is constant for Ts k ≤ t < Ts k + Ts for constant Ts and k = 0, 1, 2, ...,
then y(Ts k) is exactly described by the discrete-time state-space system
z(Ts k + Ts ) = Φz(Ts k) + Γu(m−1) (Ts k)
y(Ts k) = C̃z(Ts k) + D̃u
(m−1)
(Ts k).
(2.139a)
(2.139b)
where
Φ = eÃTs
ZTs
Γ=
eÃτ dτ B̃
0
and Ã, B̃, C̃, and D̃ are defined in (2.124) or (2.128).
(2.140)
(2.141)
38
2
Modeling
Note that there are other assumptions on the behavior of u(m−1) (t) between the sample points which also will allow us to calculate an exact discrete-time description. One
such assumption is that it is piecewise linear.
If the internal variables do not depend on derivatives of the input, we will have N D =
0 in the equations above. The derivations in this section are of course valid also for this
case, although many of the formulas can be written in a simpler form. For example, we
will have m = 1, so we do not need to redefine the input. However, note that the matrix
E in the linear DAE system may very well be singular even if there is no dependence on
derivatives of the input, so it is still advantageous to use the formulas above to write the
system in state-space form and sample it.
For state-space systems, it is typically assumed that the input (and not one of its
derivatives) is piecewise constant when sampling a system. For DAEs where the internal variables depend on derivatives of the input, this is not a realistic assumption since the
internal variables would be derivatives of a step function.
2.4
Linear Time-Varying DAE Models
A more general form of the linear DAE is the linear time-varying DAE,
E(t)ẋ(t) = A(t)x(t) + f (t)
y(t) = C(t)x(t).
(2.142a)
(2.142b)
E(t) and A(t) are square time-varying matrices and C(t) is a rectangular time-varying
matrix. The external function f (t) typically represents an input signal which for example
can enter the equations as f (t) = B(t)u(t) where u(t) is an input signal. We will stick
with the notation f (t) in this section to be consistent with the notation by Kunkel and
Mehrmann (1994) from which we will present some results. (Time-varying linear DAE
models are also discussed in the book by Kunkel and Mehrmann, 2006). The results
from Kunkel and Mehrmann (1994) which we will review here treat canonical forms for
linear time-varying DAE systems. The canonical forms of are, not surprisingly, similar to
the time-invariant case. The main difference is that time-varying transformations are used,
that is (2.142a) is multiplied from the left with a matrix P (t) and a variable transformation
x(t) = Q(t)x̃(t) is made. Since
˙
ẋ(t) = Q̇(t)x̃(t) + Q(t)x̃(t)
(2.143)
the transformed system is
˙
P (t)E(t)Q(t)x̃(t)
= P (t)A(t)Q(t) − P (t)E(t)Q̇(t) x̃(t) + P (t)f (t)
y(t) = C(t)Q(t)x(t).
(2.144a)
(2.144b)
We see that there is an extra term P (t)E(t)Q̇(t) in (2.144a) compared to the timeinvariant case. This makes the transformations somewhat more involved.
First we will need to define a few quantities related to the n × n matrices E(t)
2.4
39
Linear Time-Varying DAE Models
and A(t). Let
T (t) basis of kernel E(t)
(2.145a)
Z(t) basis of corange E(t)
T 0 (t) basis of cokernel E(t)
(2.145b)
(2.145c)
V (t) basis of corange Z ∗ (t)A(t)T (t) .
(2.145d)
A∗ is the conjugate transpose of the matrix A. The kernel (or null space), range, corange,
and cokernel of an n × n matrix A are defined as
kernel A = {y ∈ Rn |Ay = 0}
range A = {y ∈ Rn |y = Ax, x ∈ Rn }
corange A = kernel A∗
cokernel A = range A∗ .
Now, let
r(t) = rank E(t)
(2.146a)
∗
a(t) = rank Z (t)A(t)T (t)
∗
∗
(2.146b)
0
s(t) = rank V (t)Z (t)A(t)T (t)
(2.146c)
d(t) = r(t) − s(t)
b(t) = n − r(t) − a(t) − s(t).
(2.146d)
(2.146e)
The quantities r(t), a(t), s(t), d(t), and b(t) are called rank, algebraic part, strangeness,
differential part, and undetermined part respectively. We can now state the main transformation theorem from Kunkel and Mehrmann (1994).
Theorem 2.5
Let the matrices E(t) and A(t) be sufficiently smooth and let
r(t) ≡ r
(2.147a)
a(t) ≡ a
(2.147b)
s(t) ≡ s.
(2.147c)
Then there exist non-singular transformation matrices P (t) and Q(t) such that


Is 0 0 0 0
 0 Id 0 0 0



P (t)E(t)Q(t) = 
(2.148)
 0 0 0 0 0
 0 0 0 0 0
0 0 0 0 0
and

0
0

P (t)A(t)Q(t) − P (t)E(t)Q̇(t) = 
0
Is
0
A12 (t) 0
0
0
0
Ia
0
0
0
0

A14 (t) A15 (t)
A24 (t) A25 (t)

0
0 
.
0
0 
0
0
(2.149)
40
2
Modeling
Im is an identity matrix of size m × m. The sizes of the block rows are s, d, a, s, and b
respectively.
Proof: See Kunkel and Mehrmann (1994).
Note that this transformation means that the system in the transformed variables


x1 (t)
x2 (t)


x3 (t) = Q(t)x(t)
(2.150)


x4 (t)
x5 (t)
can be written as
ẋ1 (t) = A12 (t)x2 (t) + A14 (t)x4 (t) + A15 (t)x5 (t) + f1 (t)
(2.151a)
ẋ2 (t) = A24 (t)x4 (t) + A25 (t)x5 (t) + f2 (t)
(2.151b)
0 = x3 (t) + f3 (t)
(2.151c)
0 = x1 (t) + f4 (t)
(2.151d)
0 = f5 (t)
(2.151e)
where

f1 (t)
f2 (t)


f3 (t) = P (t)f (t).


f4 (t)
f5 (t)

(2.152)
The form (2.151) can be further transformed by differentiating (2.151d) and inserting into
(2.151a). We then get
0 = A12 (t)x2 (t) + A14 (t)x4 (t) + A15 (t)x5 (t) + f1 (t) + f˙4 (t)
ẋ2 (t) = A24 (t)x4 (t) + A25 (t)x5 (t) + f2 (t)
(2.153a)
(2.153b)
0 = x3 (t) + f3 (t)
(2.153c)
0 = x1 (t) + f4 (t)
(2.153d)
0 = f5 (t).
(2.153e)
This form can the be further transformed by applying Theorem 2.5 again. Repeating this
process until s = 0 for the transformed system (or equivalently, the size of x1 (t) is equal
to zero) leads to the following theorem (Kunkel and Mehrmann, 1994).
Theorem 2.6
Let Ei (t), Ai (t) be the sequence of matrices that is obtained by repeatedly applying
Theorem 2.5 and differentiating to obtain the form (2.153) for a linear time-varying DAE
(2.142a). Let si (t) be the strangeness for each pair of matrices. Let the strangeness index
defined by
m = min{i|si = 0}
(2.154)
2.5
41
DAE Solvers
be well determined. Let the function f (t) be sufficiently differentiable. Then (2.142a) is
equivalent to a differential-algebraic equation in the form
ẋ1 (t) = A13 (t)x3 (t) + f1 (t)
(2.155a)
0 = x2 (t) + f2 (t)
(2.155b)
0 = f3 (t).
(2.155c)
Here, f1 (t) is determined by the function f (t), and f2 (t) and f3 (t) are determined by
f (t), f˙(t), . . . , f (m) (t).
Proof: See Kunkel and Mehrmann (1994).
Equivalence here means that there is a one-to-one relationship between the solutions.
The fact that f1 (t) does not depend on derivatives of f (t) is not directly stated by Kunkel
and Mehrmann (1994), but it is given by the transformations involved. Each transformation has matrices Qi (t) and Pi (t) such that


x1 (t)
x(t) = Q1 (t)Q2 (t) · · · Qm (t) x2 (t)
(2.156)
x3 (t)
and


f1 (t)
f2 (t) = Pm+1 (t)P̃m (t, d ) · · · P̃1 (t, d )f (t)
dt
dt
f3 (t)
where

I
0

P̃i = 
0
0
0
0
I
0
0
0
0
0
I
0
0
d
dt I
0
0
I
0

0
0

0
 Pi (t).
0
I
(2.157)
(2.158)
d
The matrix containing dt
represents the differentiation that took us to the form (2.153).
Note that no differentiation is performed in the final transformation Pm+1 (t) since si = 0.
It is not apparent from this notation that f1 (t) does not depend on derivatives of f , but
this is given by the proofs in Kunkel and Mehrmann (1994) where block rows containing differentiated variables ẋ(t) are not mixed with rows not containing differentiated
variables.
Theorem 2.6 makes it possible to define regularity for linear time-varying DAEs as
the absence of undetermined variables x3 (t).
Definition 2.8 (Regularity). The linear time-varying DAE is said to be regular if there
are no undetermined variables, or equivalently, x3 (t) in (2.155) is of size zero.
2.5
DAE Solvers
This section introduces the basic functionality of DAE solvers, and assumptions about
the solver that will be needed to derive some of the results in the thesis. The basic functionality that is assumed is that given a nonlinear DAE, F (ẋ(t), x(t), t) = 0, the solvers
produce x(t) for a desired time interval.
42
2
Modeling
Purely numeric solvers for DAEs only handle limited classes of DAEs, usually systems with differential index 1, or limited classes of higher index systems. One common
numerical solver is DASSL (Brenan et al., 1996). For component-based models, it is not
sufficient to treat lower index problems, so instead the kind of solvers that are used to
simulate component-based models, such as Modelica models, are used. Such solvers are
included in, e.g., Dymola and OpenModelica. These solvers typically reduce the index
to 1 by differentiating equations that are chosen using Pantelides’s algorithm (Pantelides,
1988) and structure the equations so that large DAE systems can be simulated efficiently.
Then a numerical solver is used. The number of times that
Pantelides’s algorithm (Pantelides, 1988) is an important tool for finding which equations to differentiate when reducing the index of large-scale higher index DAE systems.
This is a graph-theoretical algorithm that originally was developed to find conditions that
consistent initial values must satisfy. It has later been used by others to find differentiations to reduce the index of DAE systems to 1 or 0 in DAE solvers. The algorithm only
uses structural information about which variables that are included in which equations.
While the algorithm works well for index reduction in most cases, it can sometimes give
incorrect results (Reißig et al., 2000).
Structuring of the equations to achieve efficient simulation can be performed by transforming the equations into block lower triangular (BLT) form. This means that the equations are sorted so that the equations can be solved stepwise for a few variables at a time.
An implementation of the BLT algorithm (not in connection with equation solving) is
discussed by Duff and Reid (1978).
During the index reduction process, some of the variables x(t) are selected as states.
For the user, this means that initial values of these variables can be selected independently
of each other. The initial values of the remaining variables are computed from the initial
values of the states so that the initial value is consistent. It is possible for the user to
influence the state selection process by indicating that some variables are preferred as
states.
The solver typically also structures the equations as
F̃1 (t, x1 , x3 , ẋ1 ) = 0
(2.159a)
F̂2 (t, x1 , x3 ) = 0
(2.159b)
where x3 can be solved from (2.159b) and ẋ1 can be solved from (2.159a). This means
that an approximation of the transformations discussed in Section 2.2 is computed.
One method to combine these methods into an index reducing pre-processing algorithm is suggested by Mattsson and Söderlind (1993). We summarize this algorithm here.
1. Differentiate the equations using Pantelides’s algorithm to achieve an index 1 system.
2. Permute the equations and variables into BLT form.
3. Select state variables using the dummy derivatives method (Mattsson and Söderlind, 1993).
To derive some of the identifiability results later in the thesis, some assumptions on
the DAE solver are needed:
2.6
Linear Difference-Algebraic Equations
43
• If locally unique solutions to the DAE exist, one of them is given. Otherwise the
user is notified, e.g., through an error message. There are different error messages
for the cases when no solution exists and when existing solutions are not locally
unique.
• Some of the variables that appear differentiated are selected as states by the solver.
The initial values of these variables can be selected freely by the user, and the initial
values of the remaining variables are computed from the initial values of the state
variables.
• The number of unknowns must be the same as the number of equations. (The
derivative of a variable does not count as an unknown of its own.)
These assumptions represent a kind of ideal DAE solver, but existing DAE solvers such
as the one in Dymola come quite close to satisfying them.
2.6
Linear Difference-Algebraic Equations
In this section the difference-algebraic system
Ex(t + 1) = Jx(t) + Ku(t)
y(t) = Lx(t)
(2.160a)
(2.160b)
will be treated. Difference-algebraic equations are also known as discrete-time descriptor
systems. Since the sampled version of a linear DAE system can be written as a discretetime state-space system (see Section 2.3.6), there are probably fewer applications for
discrete-time descriptor systems than for discrete-time state-space systems. However,
applications could be found among truly discrete-time systems such as some economical
systems. Discrete-time and continuous-time descriptor systems can be treated in a similar
fashion, so the discussion here will be rather brief.
We will show how (2.160) can be written in different canonical forms and then transformed into state-space form, but we can directly note that (2.160) is a discrete-time linear
system with the transfer function
G(z) = L(zE − J)−1 K.
(2.161)
A difference between G(z) and the transfer function of a discrete-time state-space system
is that G(z) here may be non-proper, that is have higher degree in the numerator than in
the denominator. This corresponds to a non-causal system. For an example of matrices E
and J that give a non-proper system, see (2.79).
Similarly to the continuous-time case, the transfer function is only well-defined if
(zE − J) is non-singular. In the next section we will define non-singularity of this matrix
as regularity for the corresponding system and show that the system is solvable if the
system is regular.
44
2.6.1
2
Modeling
Regularity
A basic assumption that will be made about the discrete-time descriptor systems is that
the inverse in (2.161) is well-defined, and below this is formalized with a definition.
Definition 2.9 (Regularity). The discrete-time descriptor system
Ex(t + 1) = Jx(t) + Ku(t)
y(t) = Lx(t)
(2.162a)
(2.162b)
is called regular if
det(zE − J) 6≡ 0,
(2.163)
that is the determinant is not zero for all z.
This definition is the same as the one used by Dai (1989b). As in the continuoustime case, regularity is equivalent to the existence of unique solution. This is discussed
by for example Luenberger (1978) and Dai (1989b). To illustrate this we examine the z
transform of equation (2.162a):
(zE − J) Z [x(t)] = KZ [u(t)] + zEx(0)
(2.164)
Z[·] represents the z transform of the argument. From this equation we can draw the
conclusion that there exists a unique solution Z[x(t)] almost everywhere if and only if
the system is regular.
2.6.2
A Canonical Form
In this section we present a transformation for discrete-time descriptor systems, which
gives a canonical form similar to the one for the continuous-time case presented in Section 2.3.3. The only difference between the two forms is actually that the derivatives in
the continuous-time case are replaced by time shifts in the discrete-time case.
Theorem 2.7
Consider a system
Ex(t + 1) = Jx(t) + Ku(t)
y(t) = Lx(t).
(2.165a)
(2.165b)
If (2.165) is regular, its solution can be described by
x1 (t + 1) = Ax1 (t) + Bu(t)
x2 (t) = −Du(t) −
m−1
X
N i Du(t + i)
(2.166a)
(2.166b)
i=1
x1 (t)
= Q−1 x(t)
x2 (t)
x (t)
y(t) = LQ 1
.
x2 (t)
(2.166c)
(2.166d)
2.6
45
Linear Difference-Algebraic Equations
The proof is the same as the one for Theorem 2.3 with all derivatives replace with
time shifts (also in the required lemmas), so it is omitted.
Note that the system is non-causal in the general case as the output can depend on
future values of the input. However, if
ND = 0
(2.167)
the system is causal.
2.6.3
State-Space Form
As mentioned earlier, state-space systems are much more thoroughly treated in the literature than descriptor systems are. This is also true for the discrete-time case, so in
this section we examine how a discrete-time descriptor system can be transformed to a
discrete-time state-space system.
We assume that the system has been converted into the form
x1 (t + 1) = Ax1 (t) + Bu(t)
x2 (t) = −Du(t) −
m−1
X
(2.168a)
N i Du(t + i)
(2.168b)
i=1
x1 (t)
= Q−1 x(t)
x2 (t)
x1 (t)
,
y(t) = LQ
x2 (t)
(2.168c)
(2.168d)
which according to Theorem 2.7 is possible if the system is regular. If m = 1, we directly
get the state-space description
x1 (t + 1) = |{z}
A x1 (t) + |{z}
B u(t)
Ã
(2.169a)
B̃
0
I
y(t) = LQ
x1 (t) + LQ
u(t).
−D
0
| {z }
| {z }
C̃
(2.169b)
D̃
If m > 1 we begin by defining a vector with time shifted inputs, corresponding to Equation (2.125):


u(t)
 u(t + 1) 


x3 (t) = 
(2.170)

..


.
u(t + m − 2)
To include x3 (t) in the state vector, the time shifted version of it must be calculated:
 

 

u(t + 1)
0 I ... 0
0
 u(t + 2)   .. .. . .
 .. 
.. 

 . .
 
. .
x3 (t+1) = 
=
 x3 (t)+  .  u(t+m−1) (2.171)
..




0
.
0 0 ... I
u(t + m − 1)
0 0 ... 0
I
46
2
Modeling
Now (2.168) can be rewritten to depend on x3 (t) instead of depending directly on the
time shifted versions of u(t). The new description of the solutions will be
x1 (t + 1) = Ax1 (t) + B 0 . . . 0 x3 (t)
(2.172a)
m−2
D
N
D
.
.
.
N
D
x2 (t) = −
x3 (t)
(2.172b)
m−1
−N
Du(t + m − 1)


 
0 I ... 0
0
 .. .. . .
 .. 
.. 

 
. .
x3 (t + 1) =  . .
(2.172c)
 x3 (t) +  .  u(t + m − 1)
0 0 . . . I 
0
0 0 ... 0
I
x (t)
y(t) = LQ 1
.
(2.172d)
x2 (t)
The final step to get a state-space description is to eliminate x2 (t) from these equations.
The elimination is performed by inserting (2.172b) into (2.172d):


 
A B 0 ... 0
0

0
0
0
I
.
.
.
0


 

x1 (t + 1)

.. .. . .
..  x1 (t) +  ..  u(t + m − 1)
=  ...
(2.173a)
.

. .  x (t)
. .
x3 (t + 1)

 
3
 0 0 0 . . . I
0
0 0 0 ... 0
I
| {z }
|
{z
}
Ã
B̃
0
−D
0
...
0
x1 (t)
+
−N D . . . −N m−2 D
x3 (t)
{z
}
0
I
y(t) = LQ
0
|
C̃
(2.173b)
LQ
u(t + m − 1)
−N m−1 D
{z
}
|
D̃
If we let
z(t) =
x1 (t)
x3 (t)
(2.174)
this can be written in the compact form
z(t + 1) = Ãz(t) + B̃u(t + m − 1)
y(t) = C̃z(t) + D̃u(t + m − 1).
(2.175a)
(2.175b)
The state-space description will in this thesis be used for estimation. However, it could
also have other applications, such as control design.
2.7
Stochastic Models
As discussed in the introduction of the chapter, it is often appropriate to model disturbances using stochastic processes. This section summarizes some results on the definition
2.7
Stochastic Models
47
and properties of stochastic processes that will be needed for the discussions later in the
thesis. Most of the results summarized here can be found in, e.g., the books by Åström
(1970) and Papoulis (1977).
2.7.1
Stochastic Processes
A stochastic process can be defined as a family of stochastic variables indexed by a set
T , {x(t), t ∈ T }. The set T will be interpreted as time in this thesis. When it takes
discrete values, T = {. . . , −1, 0, 1, . . . } or T = {0, 1, . . . }, the process x is called a
discrete-time process. When T takes continuous values, T = {t; −∞ < t < ∞} or
T = {t; 0 ≤ t < ∞}, the process is called a continuous-time process.
We need to define a number of properties for a stochastic process. The mean value is
defined as
m(t) = E x(t).
(2.176)
Furthermore, the covariance function for the processes {x(t), t ∈ T } and {y(t), t ∈ T }
is defined as
T
rxy (s, t) = cov x(s), y(t) = E x(s) − E x(s) y(t) − E y(t) .
(2.177)
If {x(t), t ∈ T } and {y(t), t ∈ T } are the same, the function rxx (s, t) is called the
autocovariance function. It is then also denoted rx (s, t), or simply r(s, t) when it is clear
to which process it belongs. The variance of a stochastic process is
var x(t) = rx (t, t).
(2.178)
A process {x(t), t ∈ T } is said to be of second order if E x2 (t) < ∞ for all t ∈ T .
A stochastic process is said to be Gaussian (or normal ) if the joint distribution of
x(t1 ), x(t2 ), . . . , x(tk ) is Gaussian for every k and all ti ∈ T , i = 1, 2, . . . , k. A
Gaussian process is completely characterized by its mean value and covariances. A process is said to be stationary if the distribution of x(t1 ), x(t
2 ), . . . , x(tk ) is the
same as the distribution of x(t1 + τ ), x(t2 + τ ), . . . , x(tk + τ ) for all ti ∈ T and all
τ such that ti + τ ∈ T . A process is said to be weakly stationary if the mean values and
covariances but not necessarily the distributions are the same. Note especially that the
covariance function can be written r(s, t) = r(s − t) for weakly stationary processes.
A process {x(t), t ∈ T } where x(tk ) − x(tk−1 ), x(tk−1 ) − x(tk−2 ), . . . , x(t2 ) −
x(t1 ), x(t1 ) for t1 < t2 < · · · < tk are mutually independent is called a process with
independent increments. Processes with independent increments can be used to define
a Wiener process or a Brownian motion process which is a process which satisfies the
following conditions:
1. x(0) = 0
2. x(t) is Gaussian
3. E x(t) = 0 for all t > 0
4. The process has independent stationary increments
48
2
Modeling
The spectral density function or spectral density φ(ω) of a weakly stationary process
describes its frequency content. In this thesis it is also called spectrum. For a continuoustime process with autocovariance function r(t) it is defined as
1
φ(ω) =
2π
Z∞
e−iωt r(t) dt
(2.179a)
−∞
Z∞
eiωt φ(ω) dω
r(t) =
(2.179b)
−∞
and for a discrete-time process with autocovariance function r(t) it is defined as
φ(ω) =
∞
1 X −iωn
e
r(n)
2π n=−∞
Zπ
r(n) =
eiωn φ(ω) dω.
(2.180a)
(2.180b)
−π
When r is a covariance function for two processes x and y, rxy , φxy is called the cross
spectral density.
A weakly stationary process with constant spectral density φ(ω) ≡ φ is called white
noise. This definition applies both for discrete-time and continuous-time processes. White
noise for continuous-time processes requires a more involved analysis than the discretetime case, but we will not go into the details here. That there are special problems with
continuous-time white noise can for example be realized from (2.179b) which gives that
the variance r(t) is infinite if φ(ω) is constant. The reader is referred to, e.g., the book by
Åström (1970) for further discussions on this.
2.7.2
Continuous-Time Linear Stochastic Models
As discussed in Section 2.1.2, we would like to define a stochastic differential equation,
SDE, according to
ẋ(t) = Ax(t) + Kv(t)
(2.181)
where v(t) is a stochastic process. We here omit the deterministic input u(t) in the notation since it does not affect the results discussed here. When {v(t), t ∈ T } is continuoustime white noise with spectrum R1 , which also can be seen as covariance 2πR1 δ(t), v(t)
has infinite variance. This means that ẋ(t) would not be well-defined. We instead have
to interpret the expression (2.181) with v(t) white noise as a stochastic integral (Åström,
1970). To point this out, the notation
dx = Axdt + Kdv
(2.182)
where {v(t), t ∈ T } is a Wiener process with incremental covariance R1 dt can be used.
The solution of the stochastic integral can be interpreted booth as an Itô integral and
as a Stratonovich integral (Åström, 1970). Irrespective of which integral concept that is
used, the solution is characterized by the following theorem.
2.7
49
Stochastic Models
Theorem 2.8
Assume that the initial value x(t0 ) of the stochastic differential equation (2.182) is a
Gaussian stochastic variable with mean m0 and covariance matrix R0 and that v(t) is
a Wiener process with incremental covariance R1 dt. The solution of the SDE is then a
normal process with mean value mx (t) and covariance R(s, t) where
dmx
= Amx
dt
mx (t0 ) = m0
(
Φ(s; t)P (t)
s≥t
R(s, t) =
P (s)ΦT (t; s) s ≤ t
dP
= AP + P AT + KR1 K T
dt
P (t0 ) = R0
dΦ(t; t0 )
= AΦ(t; t0 )
dt
Φ(t0 , t0 ) = I.
(2.183a)
(2.183b)
(2.183c)
(2.183d)
(2.183e)
(2.183f)
(2.183g)
Proof: See Åström (1970).
We would also like to use a transfer function description of a stochastic process, that
is to write a stochastic process {y(t), t ∈ T } as
y(t) = G(p)w(t)
(2.184)
where G(p) is a transfer function and {w(t), t ∈ T } is a stochastic process with spectrum
φw (ω). This can also be written as a convolution integral,
Zt
h(s − t)w(s) ds
y(t) =
(2.185)
−∞
where h(t) is the impulse response corresponding to the transfer function G(p). If w(t)
has finite variance, i.e., it is not white noise, this integral has a well-defined solution,
which is given in the following theorem.
Theorem 2.9
Suppose that a time-invariant dynamical system has the scalar transfer function G. Suppose that the input signal is a weakly stationary stochastic process with mean value mw
and spectral density φw (ω). If the dynamical system is asymptotically stable and if
Z∞
φw (ω) dω ≤ a < ∞
rw (0) =
(2.186)
−∞
then the output signal (after transients have disappeared) is a weakly stationary process
with mean value
my = G(0)mw
(2.187)
50
2
Modeling
and spectral density
φy (ω) = G(iω)G(−iω)φw (ω).
(2.188)
The input-output cross spectral density is
φwy (ω) = G(−iω)φw (ω).
(2.189)
Proof: See Åström (1970).
If w(t) is to be interpreted as white noise in (2.184), then (2.184) must be seen as
shorthand notation for the stochastic integral
Zt
h(t − s) dw(s)
y(t) =
(2.190)
∞
where h is the impulse response of the transfer function G and {w(t), t ∈ T } is a Wiener
process with orthogonal increments. If we want to select a linear filter so that y(t) has a
certain spectrum, the following theorem can be used (Åström, 1970).
Theorem 2.10
Consider a rational spectral density function φ(ω). There exists an asymptotically stable,
time invariant dynamical system with the impulse response h such that the stochastic
process defined by
Zt
y(t) = h(t − s) dw(s)
(2.191)
∞
where {w(t), t ∈ T } is a process with independent increments, is stationary and has the
spectral density φ(ω). Furthermore, if w has incremental covariance 2πdt, the transfer
function G corresponding to h can be chosen as
φ(ω) = G(iω)G(−iω)
(2.192)
with all poles in the left half plane and all zeros in the left half plane or on the imaginary
axis.
Proof: See Åström (1970).
2.7.3
Discrete-Time Linear Stochastic Models
It is easier to define a stochastic difference equation than a stochastic differential equation.
A linear stochastic difference equation can be written as
x(t + 1) = Ax(t) + Kv(t)
(2.193)
where the process {v(t), t ∈ T } is discrete-time white noise. When v(t) is Gaussian with
mean value zero and covariance matrix R1 , the solution is characterized by the following
theorem (Åström, 1970).
2.7
51
Stochastic Models
Theorem 2.11
The solution of the stochastic difference equation (2.193) where the initial value is a
Gaussian random variable with mean m0 and covariance matrix R0 and v(t) is Gaussian
with mean value zero and covariance R1 , is a Gaussian random process with the mean
value
m(t + 1) = Am(t)
(2.194)
with the initial condition
m(t0 ) = m0
(2.195)
R(s, t) = As−t P (t) s ≥ t
(2.196)
P (t + 1) = AP (t)AT + KR1 K T
(2.197)
P (t0 ) = R0 .
(2.198)
and the covariance function
where P (t) satisfies
with the initial condition
Proof: See Åström (1970).
Also the transfer function description is easier to handle for discrete-time systems than
for continuous-time systems. Consider the description
y(t) = H(q)u(t)
(2.199)
where {u(t), t ∈ T } is a stationary process. This can also be written as a convolution,
y(t) =
t
X
h(t − s)u(s).
(2.200)
s=−∞
The solution is characterized by the following theorem (Åström, 1970).
Theorem 2.12
Consider a stationary discrete-time system with the transfer function H(z). Let the input signal be a stationary stochastic process with mean value mu and the spectral density φu (ω). If the system is asymptotically stable, then the output signal is a stationary
stochastic process with mean value
my = H(1)mu
(2.201)
φy (ω) = H(e−iω )H(eiω )φu (ω) = |H(eiω )|2 φu (ω).
(2.202)
and spectral density
The cross spectral density between input and output is given by
φyu (ω) = H(e−iω )φu (ω)
Proof: See Åström (1970).
(2.203)
52
2
Modeling
Note that this theorem also holds for white noise inputs, that is constant φu (ω).
It is possible to transform a continuous-time stochastic differential equation for which
the output is measured at sampling instants into a discrete-time system. The following
lemma describes how this transformation is performed (Åström, 1970).
Lemma 2.5
Consider a state-space system with noise model
dx = Axdt + dv
(2.204a)
dy = Cxdt + de
(2.204b)
where v and e are Wiener processes with incremental covariances
E dv dv T = R1 dt
E dv deT = R12 dt
E de deT = R2 dt.
(2.205a)
(2.205b)
(2.205c)
The values of the state variables and the outputs of the state-space system at discrete times
kTs , k = 1, 2, . . . , are related through the stochastic difference equations
x(Ts k + Ts ) = Φx(Ts k) + v̄(Ts k)
(2.206a)
z(Ts k + Ts ) = y(Ts k + Ts ) − y(Ts k) = θx(Ts k) + ē(Ts k)
(2.206b)
Φ = eATs
(2.207a)
where
ZTs
θ=C
eAτ dτ
(2.207b)
0
and the discrete stochastic variables v̄(t) and ē(t) have zero mean values and the covariances
T
E v̄(t)v̄ (t) = R̄1 =
ZTs
T
eA(Ts −τ ) R1 eA(Ts −τ ) dτ
(2.208a)
0
T
E v̄(t)ē (t) = R̄12 =
ZTs
eA(Ts −τ ) R1 ΘT (τ ) + R12 dτ
(2.208b)
0
E ē(t)ēT (t) = R̄2 =
ZTs
T
Θ(τ )R1 ΘT (τ ) + Θ(τ )R12 + R12
ΘT (τ ) + R2 dτ
0
(2.208c)
ZTs
Θ(τ ) = C
eA(s−τ ) ds.
τ
Proof: See Åström (1970).
(2.208d)
2.8
Conclusions
2.7.4
53
Nonlinear Stochastic Models
For nonlinear stochastic models, we will limit the discussion to the case when white noise
v(t) enters affinely into the equations,
ẋ(t) = f x(t), t + σ x(t), t v(t).
(2.209)
That the noise enters affinely into the equations is of course a special case of a more general model structure where the noise enters through a general nonlinear function. However, the general case is less treated in the literature. Since our goal is to extend existing
results for state-space models to DAE models, the discussion is limited to the special case
(2.209).
As in the linear case, (2.209) must be treated as a stochastic integral (Åström, 1970)
To point this out, the notation
dx = f x(t), t dt + σ x(t), t dv,
(2.210)
where v(t) is a Wiener process, is used.
Stochastic state models can be used for example for simulation and for state estimation
using nonlinear filtering methods such extended Kalman filters (e.g., Kailath et al., 2000;
Gustafsson, 2000) and particle filters (e.g., Gordon et al., 1993; Doucet et al., 2001; Ristic
et al., 2004). Particle filters are also discussed in Chapter 4 of this thesis.
2.8
Conclusions
We introduced the concept of component-based modeling, and saw that this in the general case leads to a differential-algebraic equation (DAE). We discussed general theory
about DAE models, including the analysis method by Kunkel and Mehrmann (2001).
This theory shows that provided the DAE satisfies Property 2.1, one part of the variables
is determined by state-space equations, and one part of the variables is determined by
algebraic equations. One part of the variables may also be undetermined. If no variables
are undetermined, the DAE is called regular. We also discussed how large DAE systems
can be solved in practice.
For linear DAE systems, we presented the concept of regularity and noted that it is
equivalent to the existence of a unique solution. We also discussed a canonical form that is
well-known in the literature, and provided a proof that will allow numerical computation
as will be discussed in Chapter 11. This canonical form was then used to derive a statespace description. To get this state-space description, the input may have to be redefined
as one of its derivatives in the continuous-time case or future values in the discrete-time
case. For the continuous-time case, the state-space description was then used to sample
the system.
We also discussed stochastic models, and properties of stochastic models that will be
needed in the thesis.
54
2
Modeling
3
System Identification
The main topic of this thesis is estimation of unknown parameters in differential-algebraic
equation models. This is an application of system identification, so in this chapter basic
properties of system identification are discussed. The different methods are only discussed briefly. For a more thorough discussion, the reader is referred to, e.g., Ljung
(1999).
3.1
Prediction Error Methods
System identification is about estimating models from measured input data and output
data. The measured data set is denoted Z N ,
Z N = {u(t0 ), y(t0 ), ..., u(tN ), y(tN )}
(3.1)
where u are inputs to the system and y outputs from the system. To estimate models, we
use equations that are parameterized using parameters θ. We thus have a model structure
that is known apart from the values of the constant parameters θ. In this thesis we are
concerned with gray-box models where the parameters θ have a physical interpretation
(Bohlin, 1991; Graebe, 1990; Ljung, 1999).
These unknown parameters are selected such that the measured data and the solution
of the equations fit as closely as possible. This produces an estimate θ̂. A standard way
to compare the measured output with the solution of the equations, is to consider the
model’s prediction of the output at each time point, given the data up to but not including
that time point. This leads to the prediction error method. We are thus interested in the
one-step-ahead predictor,
ŷ(tk |tk−1 , θ).
(3.2)
This is the prediction of y(tk ) given Z k−1 (and u(tk ), u(tk+1 ), . . . , if it is necessary)
using the model corresponding to the parameter value θ. The prediction errors ε(tk , θ) are
55
56
3
System Identification
then the difference between the predicted outputs ŷ(tk |tk−1 , θ) and the measured outputs
y(tk ):
ε(tk , θ) = y(tk ) − ŷ(tk |tk−1 , θ)
(3.3)
The parameters are estimated by minimizing a norm of the prediction errors. One common choice is the quadratic criterion
VN (θ, Z N ) =
N
1 X1 T
ε (tk , θ)Λ−1 ε(tk , θ)
N
2
(3.4)
k=1
for some positive definite matrix Λ that is chosen according to the relative importance of
the components of ε(tk , θ). The parameter estimate θ̂ is then computed as
θ̂ = arg min VN (θ, Z N ).
(3.5)
θ
The minimization is typically performed by a numerical search method, for example
Gauss-Newton (Ljung, 1999).
Depending on the model structure, the predictor ŷ(tk |tk−1 , θ) is computed in different
ways. Below we will list the cases that are of interest in this thesis.
• For linear discrete-time state-space models,
x(tk+1 ) = A(tk )x(tk ) + B(tk )u(tk ) + K(tk )v1 (tk )
y(tk ) = C(tk )x(tk ) + v2 (tk )
(3.6a)
(3.6b)
where v1 and v2 are white noise processes, the predictor is computed using the
Kalman filter (e.g., Kalman, 1960; Anderson and Moore, 1979; Kailath et al., 2000).
• For linear continuous-time state-space models with discrete-time measurements,
ẋ(t) = A(t)x(t) + B(t)u(t) + K(t)v1 (t)
y(tk ) = C(tk )x(tk ) + v2 (tk )
(3.7a)
(3.7b)
where v1 and v2 are white noise processes, the predictor is computed using the
Kalman filter for continuous-time systems with discrete-time measurements (e.g.,
Jazwinski, 1970).
• For nonlinear state-space systems with only measurement noise (output-error models)
ẋ(t) = f x(t), u(t), θ
(3.8a)
y(tk ) = h x(tk ), θ + e(tk ).
(3.8b)
the predictor is computed by simulating the system.
• For nonlinear state-space systems with a more general noise model,
ẋ(t) = f x(t), u(t), θ + σ x(t), u(t), θ w(t)
y(tk ) = h x(tk ), θ + e(tk ).
(3.9a)
(3.9b)
3.2
57
The Maximum Likelihood Method
the solution of the prediction problem is a infinite-dimensional nonlinear filter (e.g.,
Wong and Hajek, 1985, Chapter 5). However, there are approximate methods such
as extended Kalman filters (e.g., Gustafsson, 2000) and particle filters (Gordon
et al., 1993; Doucet et al., 2001; Ristic et al., 2004). Particle filters are further
discussed in Chapter 4.
For all these cases, the initial condition x(t0 ) must be considered as known or estimated
along with the parameters θ.
3.2
The Maximum Likelihood Method
The maximum likelihood method estimates the unknown parameters by maximizing the
probability of the measured output with respect to the unknown parameters. Given that
the measured signals have the probability density function fy (θ, Z N ), the parameters are
estimated as
θ̂ = arg max fy (θ, Z N ).
(3.10)
θ
Maximizing the likelihood function is equivalent to maximizing the log-likelihood function log fy (θ, Z N ). The parameters are then estimated as
θ̂ = arg max log fy (θ, Z N ).
(3.11)
θ
The likelihood function can be computed using the likelihood function of the predictors (Ljung, 1999, Lemma 5.1). For linear state-space systems with Gaussian noise processes, the likelihood function can also be computed directly using the Kalman filter. For
nonlinear state-space models, the likelihood function can for example be approximated
using the particle filter. This is discussed, e.g., by Andrieu et al. (2004).
3.3
Frequency Domain Identification
Frequency domain methods aim to estimate the unknown parameters θ from frequency
domain data,
Z N = {U (ω1 ), Y (ω1 ), . . . , U (ωN ), Y (ωN )},
(3.12)
where Y (ωk ) and U (ωk ) are the discrete Fourier transforms of the corresponding time
domain signals in the discrete-time case, or approximations of the Fourier transforms in
the continuous-time case. References on frequency domain identification are e.g., Ljung
(1999, Section 7.7) and Pintelon and Schoukens (2001). The Y (ωk ) and U (ωk ) can
be obtained directly from the system using a measurement device providing frequency
domain data, or calculated from time domain data. Here we will consider frequency
domain identification for linear systems described by transfer functions,
y(t) = G(p, θ)u(t) + H(p, θ)e(t)
(3.13)
in the continuous-time case and
y(tk ) = G(q, θ)u(tk ) + H(q, θ)e(tk )
(3.14)
58
3
System Identification
in the discrete-time case where H(·, θ) is assumed to have a causal inverse. Here p is
d
the differentiation operator, px(t) = dt
x(t) and q is the time shift operator, qx(tk ) =
x(tk+1 ).
In order to estimate the parameters, a criterion like
VN (θ, Z N ) =
N
X
|Y (ωk ) − G(eiωk , θ)U (ωk )|2 Wk
(3.15)
|Y (ωk ) − G(iωk , θ)U (ωk )|2 Wk
(3.16)
k=1
in the discrete-time case, and
VN (θ, Z N ) =
N
X
k=1
in the continuous-time case is minimized. The weighting functions Wk can be selected
using the noise model H(·, θ). If the noise model depends on θ, a second term usually
has to be added to the criterion to get consistent estimates, see the book by Ljung (1999,
Equation 7.147).
It should be noted that an advantage with frequency domain identification methods is
that continuous-time models and discrete-time models can be handled in similar ways.
3.4
Identifiability
Identifiability of a model structure means that it is possible to define its parameters uniquely. Essentially it requires that different parameter values θ1 and θ2 (θ1 6= θ2 ) give
different model outputs. References on identifiability are, e.g., Walter (1982) and Ljung
(1999, Chapter 4).
When discussing identifiability, we will limit the discussion to a deterministic DAE
model with a vector of unknown parameters θ,
G ẋ(t), x(t), θ, u(t), t = 0
(3.17a)
y(t) = h x(t)
(3.17b)
where x ∈ Rnx and y ∈ Rny . As before, the parameters θ ∈ Rnθ range over the set
DM ⊆ Rnθ . Formally the following definitions of identifiability will be used.
Definition 3.1 (Global identifiability). The model (3.17) is globally identifiable at θ0 ,
x0 for the input u(t) if
θ ∈ DM
(3.18)
⇒ θ = θ0
y(θ0 , t) = y(θ, t) for all t
where y(θ̄, t) is the output y of (3.17) with the input u(t), θ = θ̄, and the consistent
initial condition x0 . The system is globally identifiable if it is globally identifiable at all
θ0 ∈ DM and consistent initial conditions x0 .
Another interesting property is local identifiability.
3.4
59
Identifiability
Definition 3.2 (Local identifiability). The model (3.17) is locally identifiable at θ0 , x0
for the input u(t) if there exists a neighborhood V of θ0 for which
θ∈V
(3.19)
⇒ θ = θ0
y(θ0 , t) = y(θ, t) for all t
where y(θ̄, t) is the output y of (3.17) with the input u(t), θ = θ̄, and the consistent initial
condition x0 . The system is locally identifiable if it is locally identifiable at all θ0 ∈ DM
and consistent initial conditions x0 .
Differential Algebra
If a DAE (3.17) consists only of polynomial equations, it is possible to use differential
algebra (Ritt, 1966) to examine identifiability as described by Ljung and Glad (1994).
The idea of this method is to transform the equations of the original DAE into a new set
of equations, where it is easy to see whether the model is identifiable or not.
The following result by Ljung and Glad (1994) shows how this can be done. Assume
that a model structure is specified by (3.17) where the equations are polynomials and that
the unknown parameters are time-invariant, i.e., the equations
θ̇(t) = 0
(3.20)
are included among the equations (3.17). Using Ritt’s algorithm from differential algebra (Ritt, 1966; Ljung and Glad, 1994) it is typically possible to compute a new set of
polynomial equations of the form
A1 (y, u, p) = 0
..
.
Any (y, u, p) = 0
B1 (y, u, θ1 , p) = 0
B2 (y, u, θ1 , θ2 , p) = 0
..
.
(3.21)
Bnθ (y, u, θ1 , θ2 , . . . , θnθ , p) = 0
C1 (y, u, θ, x, p) = 0
..
.
Cnx (y, u, θ, x, p) = 0
which has the same solutions as (3.17) if some conditions of the form
si x(t), y(t), u(t), θ(t), p 6= 0
i = 1, 2, . . . , ns
(3.22)
are satisfied. θ1 , θ2 , . . . , θnθ are the scalar elements of the vector θ. In this set of equations,
d
x(t). For example, the expression A1 (y, u, p)
p is the differentiation operator px(t) = dt
could also be written as A1 (y, ẏ, ÿ, . . . , u, u̇, ü, . . . ). Also note that the requirement that
60
3
System Identification
the original DAE only consists of polynomial equations is essential for Ritt’s algorithm to
be applicable.
Ritt’s algorithm is a procedure that resembles Gram-Schmidt orthogonalization or
Gauss elimination. For example, to produce A1 (y, u, p) in (3.21), it takes an arbitrary
equation of the original DAE. If this element contains unwanted features (e.g., θ occurs in
the equation), they are removed with allowed algebraic manipulations (addition or multiplication of another element or its derivative). By this procedure, a “better” element is
produced in each step, and after a finite number of steps it will find the desired form. See
further Ritt (1966); Ljung and Glad (1994).
Since (3.21) is equivalent to (3.17), it is possible to use those equations to examine
identifiability of (3.17). To do this we observe that only the Bi polynomials give information about the value of θ since the Ai polynomials do not include θ and the Ci include
the x variables. Identifiability is thus determined by the polynomials Bi in (3.21). If the
variables θ1 , θ2 ,. . . all occur exclusively in undifferentiated form in the Bi (i.e., no terms
θ̇ occur), then these polynomials give a triangular set of nonlinear equations for determining the θi . There are three cases that can occur, depending on the identifiability properties
of the model.
1. If the Bi have the form
Bi = Pi (y, u, p)θi − Qi (y, u, p),
(3.23)
i.e., a linear regression, then the model structure is globally identifiable, provided
Pi (y, u, p) 6= 0.
2. If the Bi are higher order polynomials in θ, then there is local but not global identifiability.
3. If there are equations on the form
Bi = θ̇i ,
(3.24)
then these θi are neither locally nor globally identifiable.
The method discussed in this section is illustrated in the following example.
Example 3.1: Identifiability and differential algebra
Consider the model structure
ÿ + 2θẏ + θ2 y = 0
(3.25a)
θ̇ = 0
(3.25b)
from Ljung and Glad (1994). Applying Ritt’s algorithm returns
A1 (y, p) = 4y ÿ 3 − 3ẏ 2 · ÿ 2 − 6yy (3) ẏ ÿ + 4y (3) ẏ 3 + y 2 y (3)2
B1 (y, θ, p) = (ẏ ÿ − yy
(3)
2
) + 2θ(ẏ − y ÿ).
Since B1 is a linear function in θ, the model structure is globally identifiable.
(3.26a)
(3.26b)
3.5
3.5
61
Observability
Observability
For the special case of DAE models,
G ẋ(t), x(t), u(t), t = 0
y(t) = h x(t) ,
(3.27a)
(3.27b)
observability means that it is possible to uniquely estimate the internal variables x(t) if
the input u(t) and output y(t) are known. The internal variables may implicitly depend
on derivatives of u(t), so it will be assumed that the input is infinitely differentiable.
Observability is closely related to identifiability. The conceptual connections between
observability and identifiability for nonlinear systems can be seen by noting that identifiability of θ in the DAE model
G ẋ(t), x(t), θ, u(t), t = 0
(3.28a)
y(t) = h x(t)
(3.28b)
can bee seen as observability of the (constant) variable θ(t) in the model
G ẋ(t), x(t), θ(t), u(t), t = 0
(3.29a)
θ̇(t) = 0
(3.29b)
y(t) = h x(t) .
(3.29c)
Observability is treated in books on nonlinear control systems such as the one by
Nijmeijer and van der Schaft (1990) and the one by Isidori (1989). See also the article by
Hermann and Krener (1977).
We will only discuss local weak observability, which means that the observability
property only is examined in a region around the true trajectory of the internal variables.
Formally, the following definitions are used. First, let the solution x(t) of the DAE (3.27a)
with the consistent initial condition x0 and the input u(t) be denoted π t; x0 , u(t) . Two
consistent initial conditions x1 and x2 are then indistinguishable if they give rise to the
same output, i.e.,
h π t; x1 , u(t) = h π t; x2 , u(t)
(3.30)
for all infinitely differentiable u(t). A natural definition of observability is then that if x1
and x2 are indistinguishable, then x1 = x2 . For local weak observability, which is the
case that mainly is discussed in this thesis, a more involved definition is necessary.
Definition 3.3. Let U be an open set. Two consistent initial conditions x1 and x2 which
both belong to U are said to be U -indistinguishable if they give the same outputs in all
cases where both trajectories lie entirely in U , i.e.,
h π t; x1 , u(t) = h π t; x2 , u(t) for all t ∈ [t0 , t1 ]
(3.31)
as soon as
π t; x1 , u(t) ∈ U, π t; x2 , u(t) ∈ U, t ∈ [t0 , t1 ]
(3.32)
for all infinitely differentiable inputs u(t). The set of all points that are U -indistinguishable
from x0 is denoted IU (x0 ).
62
3
System Identification
It is now possible to give the definition of local weak observability:
Definition 3.4 (Local weak observability). The system (3.27) is locally weakly observable at the consistent initial condition x0 if there exists an open neighborhood U of x0
such that for every neighborhood V of x0 with V ⊂ U , IV (x0 ) = {x0 }. If this is true for
all points x0 , the system is locally weakly observable.
Part I
Nonlinear DAE Models
63
4
Well-Posedness of Nonlinear
Estimation Problems
This chapter discusses noise modeling and estimation for nonlinear DAE systems. We
will pay special attention to well-posedness of noise models.
4.1
Introduction
When modeling physical systems, it is usually impossible to predict the exact behavior of
the system. This can have several explanations. One common situation is that it is known
that external stimuli are affecting the systems, but these signals cannot be measured or
chosen.
Example 4.1: Noise modeling: process noise
Consider an airplane with mass m flying straight ahead with velocity x(t). The force
produced by the engine is called u(t). The resistance caused by the air if there were
no wind, is the known function f (x). If there is no wind, the motion of the aircraft is
described by
mẋ(t) = u(t) − f (x).
(4.1)
If there is wind acting on the aircraft, this can be seen as an additional force w(t) acting
on the aircraft. The motion of the aircraft is then described by
mẋ(t) = u(t) − f (x) + w(t).
(4.2)
This force w(t) is an example of an external stimulus that is known to exist, but cannot
be measured.
As discussed in Section 2.1.2 and 2.7, external stimuli that are not measured are often
65
66
4
Well-Posedness of Nonlinear Estimation Problems
modeled as stochastic processes.
Another common situation is that certain signals in the system are measured, but there
are imperfections in the measurements. For example, a sensor may have an unknown
offset or produce measurements with a time-varying error.
Example 4.2: Noise modeling: measurement noise
Consider the airplane discussed in Example 4.1. Assume that a sensor is measuring the
velocity x(t) of the aircraft at time instances tk , k = 1, . . . Due to imperfections in the
measurement device, an unknown error e(tk ) is added to each measurement y(tk ). The
measurement equation is then
y(tk ) = x(tk ) + e(tk ),
k = 1, . . .
(4.3)
Also measurement noise can be modeled as a stochastic process. A third possibility is
that a model has imperfections, but that these cannot be classified as unmeasured external
stimuli or measurement imperfections.
Example 4.3: Noise modeling: model imperfections
Consider a system which is described using a state-space model,
ẋ(t) = f x(t) .
(4.4)
Assume that observations show that (4.4) is only approximately satisfied. It may then be
appropriate to include a term w(t) to indicate that the equation does not hold exactly,
ẋ(t) = f x(t) + w(t).
(4.5)
Also in this case, the properties of w(t) can be modeled as a stochastic process. We have
now identified three situations when it may be appropriate to include stochastic processes
when modeling a physical system:
• Unmeasured external stimuli are affecting the system.
• A signal is measured with an imperfect sensor.
• There are model imperfections that do not fall into the two previous categories.
As discussed in the examples above, the first and last case can often be modeled by including a stochastic process w(t) in the model. This is called process noise. The second case
is typically modeled by including a stochastic process e(tk ) in the equations describing
the measurements.
This chapter is about how we can handle the situations discussed above for DAE
models. We are thus interested in incorporating process noise w(t) and measurement
noise e(tk ) in a DAE model. In the general case, this would result in the stochastic DAE
F ẋ(t), x(t), w(t), u(t) = 0
(4.6a)
y(tk ) = h x(tk ) + e(tk )
(4.6b)
4.2
Literature Overview
67
where u is a known input and y is a measured output. If there is no process noise, the
stochastic DAE simplifies to
F ẋ(t), x(t), u(t) = 0
(4.7a)
y(tk ) = h x(tk ) + e(tk ).
(4.7b)
This is called an output-error model. Once we have established how noise can be added
to DAE models, we will also discuss how the internal variables can be estimated using
particle filters (Gordon et al., 1993; Doucet et al., 2001; Ristic et al., 2004) and also how
unknown parameters can be estimated.
4.2
Literature Overview
The question whether the state estimation problem for DAE models is well-defined has
been discussed by, e.g., Schein and Denk (1998), Winkler (2004), Darouach et al. (1997),
Kuc̆era (1986), Germani et al. (2002), and Becerra et al. (2001). In Schein and Denk
(1998), linear SDAE are treated, and it is guaranteed that the noise is not differentiated by
assuming that the system has differential index 1 (see Section 2.2). The assumption that
the system has differential index 1 is more restrictive than is necessary, and rules out some
applications such as many mechanics systems. This assumption will not be made here.
Schein and Denk (1998) also note that some internal variables actually may be so-called
generalized stochastic processes, that is a time-continuous white noise process. Winkler
(2004) makes the same assumption as Schein and Denk (1998), but also treats a class of
nonlinear DAEs.
Darouach et al. (1997) treat linear DAEs with differential index 1, and a Kalman filter is constructed. However, in the estimation procedure the authors seem to overlook
the fact that some variables may have infinite variance. In Kuc̆era (1986), the original
linear SDAE system specification may actually specify derivatives of white noise, but a
controller is designed that removes any derivatives. In Germani et al. (2002) restrictive
assumptions are made that guarantee that no derivatives appear in the linear SDAE, although this is not stated explicitly. Finally, in Becerra et al. (2001) nonlinear semi-explicit
DAEs (e.g., Brenan et al., 1996) are discussed. Here well-posedness is guaranteed by only
adding noise to the state-space part of the system.
4.3
Background and Motivation
As mentioned in the introduction, the question treated in this chapter is how unknown
disturbances can be modeled in DAEs according to
F ẋ(t), x(t), w(t), u(t) = 0
(4.8a)
y(tk ) = h x(tk ) + e(tk ),
(4.8b)
where w is process noise and e is measurement noise, and also how such models can be
used for estimation of the internal variables x(t) and constant parameters. We will limit
68
4
Well-Posedness of Nonlinear Estimation Problems
the discussion to the case when w(t) is a Gaussian second order stationary process with
spectrum
φw (ω).
(4.9)
The spectrum is assumed to be rational in ω with pole excess 2pw . This means that
lim ω 2pw φw (ω, θ) = C(θ)
ω→∞
0 < C(θ) < ∞ for θ ∈ DM .
An important property of DAE models is that the internal variables may depend on
derivatives of the inputs to the model. This can for example be realized from the discussion on linear DAE models in Section 2.3. This is one of the central points when
discussing noise for DAE models. Since w(t) occurs as an input signal in the DAE equations (4.8), one or more of its derivatives with respect to time may affect the internal
variables x(t). This is a problem, since time derivatives of a Gaussian second order stationary process may not have finite variance. Actually, w(t) can be differentiated at most
pw − 1 times since it has pole excess 2pw . This can be realized from (2.179b) which gives
dn
that the variance of dt
n w(t) is
Z∞
r(0) =
−∞
(
<∞
(iw) φw dω
=∞
2n
if n ≤ pw − 1
if n ≥ pw .
(4.10)
Example 4.4: Noise modeling difficulties
Consider the DAE


ẋ1 (t) − x2 (t)

=0
ẋ3 (t) − x2 (t)
2
2
x1 (t) + x3 (t) − 1 − w(t)
(4.11)
where a stochastic process has been added to the last equation to model an unmeasured
disturbance. Differentiating the last equation with respect to time gives
2x1 (t)ẋ1 (t) + 2x3 (t)ẋ3 (t) − ẇ(t) = 0.
(4.12)
Eliminating ẋ1 (t) and ẋ3 (t) using the first two equations of the DAE and solving for x2 (t)
gives
ẇ(t)
.
(4.13)
x2 (t) =
2x1 (t) + 2x3 (t)
If the spectrum of w(t) has pole excess 2, this is questionable since ẇ(t) then has infinite
variance. However, if the pole excess is 3 or higher, the involved signals have finite
variance.
As we saw in the example above, it is essential to examine how many derivatives of w(t)
4.3
69
Background and Motivation
that affect the internal variables. A central tool in this aspect will be the methods by
Kunkel and Mehrmann (2001) that were reviewed in Section 2.2. The result that will be
used is that given that a DAE fulfills Property 2.1, it is possible to see it as a combination
of a state-space system that determines part of the variables, and algebraic equations that
determine part of the variables. More specifically, consider the DAE
F ẋ(t), x(t), w(t), u(t) = 0
(4.14)
where we for a moment assume that all involved signals can be differentiated as many
times as necessary. If this DAE fulfills Property 2.1, then there exists matrices Z1 and Z2 ,
and a constant integer µ such that
F̂1 (x1 , x2 , x3 , ẋ1 , ẋ2 , ẋ3 , u, w) = Z1T F

(4.15a)

F
 dF 
 dt 
F̂2 (x1 , x2 , x3 , u, u̇, . . . , u(µ) , w, ẇ, . . . , w(µ) ) = Z2T  . 
 .. 
dµ
dtµ F
(4.15b)
where the notation u(µ) is used for the µ:th time derivative of the signal u. From these
equations it is then, according to the theory by Kunkel and Mehrmann (2001), possible
to solve for x3 in F̂2 = 0, and after using that equation to eliminate ẋ3 and x3 in F̂1 , the
equation F̂1 = 0 can be solved for ẋ1 .
If we now again let w(t) be a stochastic process which has a spectrum with pole excess
2pw , then it can be differentiated at most pw − 1 times. If it is differentiated pw times
or more, the resulting signal has infinite variance. This means that a sufficient condition
for the signals x in the DAE to have finite variance is that no derivatives of w higher than
pw − 1 occur in F̂2 in (4.15b). Throughout this chapter, we will assume that the DAEs are
regular, so that x2 is of size zero. This discussion leads to the following result.
Result 4.1
Consider the SDAE
F ẋ(t), x(t), w(t), u(t) = 0
(4.16)
where w(t) is a Gaussian second order stationary process with spectrum φw (ω) which
is rational in ω with pole excess 2pw . Assume that the SDAE, with w(t) considered as a
differentiable signal, fulfills Property 2.1 and is regular. The signals x(t) then have finite
variance provided that F̂2 can be written as
F̂2 = F̂2 (x1 , x2 , x3 , u, u̇, . . . , u(k) , w, ẇ, . . . , w(l) )
(4.17)
where l ≤ pw − 1 and F̂2 is defined by (4.15b).
The above discussion shows how it can be examined if a noise process w(t) is differentiated too many times so that the resulting equations include signals with infinite variance.
However, we would also like to be able to discuss solutions to stochastic DAEs in terms
of stochastic differential equations. Our approach to this will be to convert the SDAE
to the state-space form (2.210) discussed in Section 2.7.4. Then methods for stochastic
state-space systems can then be used to define the solution.
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Well-Posedness of Nonlinear Estimation Problems
The methods discussed in Section 2.7.4 require the noise process to be white noise, but
in this chapter we have so far only discussed noise w(t) with finite variance. However,
as w(t) is assumed to be a Gaussian second order stationary process, it can be seen as
white noise filtered through a linear filter (e.g., Section 2.7). The filter can for example be
written in state-space form,
ẋw (t) = Axw (t) + Bv(t)
w(t) = Cxw (t)
where v(t) is white noise. Combining the SDAE (4.8a) and (4.18) gives
F ẋ(t), x(t), Cxw (t), u(t) = 0
ẋw (t) = Axw (t) + Bv(t)
(4.18a)
(4.18b)
(4.19a)
(4.19b)
This can be seen as a single SDAE,
G ż(t), z(t), v(t), u(t) = 0
(4.20)
where v(t) is white noise and
z(t) =
x(t)
.
xw (t)
(4.21)
When the SDAE contains white noise terms, additional restrictions apply. Not only is it
not allowed to differentiate the white noise signal, but it must also be integrated in the
affine form discussed in Section 2.7.4.
Example 4.5: White noise modeling difficulties
Consider the nonlinear DAE
ẋ1 (t) − x22 (t) = 0
x2 (t) − v(t) = 0
(4.22a)
(4.22b)
where v(t) is white noise. The second equation states that x2 (t) is equal to a timecontinuous white noise process. Since such processes have infinite variance, this is questionable if x2 (t) represents a physical quantity. The first equation states that
ẋ1 (t) = v 2 (t)
(4.23)
which also is questionable since nonlinear operations on white noise cannot be handled
in the framework of stochastic integrals as discussed in Section 2.7.4.
The main topics of this chapter concern how noise can be included in DAE models without
introducing problems such as those discussed in the example and how particle filters and
parameter estimation can be implemented for DAE models with white noise inputs.
4.4
Main Results
The main result of this chapter states conditions for when a SDAE with white process
noise v,
F ẋ(t), x(t), v(t), u(t) = 0,
(4.24)
4.4
71
Main Results
can be interpreted as a stochastic differential equation, and thus has a well-defined solution. As discussed above, such models typically arise from a modeling situation where
disturbances have been modeled as second order processes w(t). These w(t) have then
been modeled as white noise v(t) filtered through a linear filter according to the following
procedure:
1. Let the process noise w(t) of an SDAE
G ż(t), z(t), w(t), u(t) = 0
(4.25)
be modeled as white noise v(t) passed through a linear filter,
żw (t) = Azw (t) + Bv(t)
w(t) = Czw (t).
2. Combine this into one SDAE with white process noise,
F ẋ(t), x(t), v(t), u(t) = 0
where
F =
G ż(t), z(t), Czw (t), u(t)
żw (t) − Azw (t) − Bv(t)
and
x(t) =
z(t)
.
zw (t)
(4.26a)
(4.26b)
(4.27)
(4.28)
(4.29)
To simplify the notation, we will let the SDAE depend directly on time t instead of on
the input u(t),
F ẋ(t), x(t), v(t), t = 0.
(4.30)
To formulate the result, we will use the functions F̂1 and F̂2 from the theory by Kunkel
and Mehrmann (2001). F̂1 and F̂2 were introduced in Section 2.2 and also discussed in
Section 4.3 of this chapter. From Section 2.2 we also recall the notation
F̂l;p
for partial derivatives of F̂l with respect to the variables p, e.g.,
∂
∂
.
F̂2;x1 ,x2 = ∂x
F̂
F̂
2
2
∂x2
1
(4.31)
(4.32)
Furthermore, we will denote the inverse of the square matrix F̂2;x3 by
−1
F̂2;x
.
3
(4.33)
Theorem 4.1
Assume that (4.30) satisfies Corollary 2.1 when v(t) is considered as a known signal
of which we can take formal derivatives. Let F̂1 , F̂2 , x1 , x2 , and x3 be defined as in
Section 2.2 and assume that the system is regular (x2 is of size zero).
72
4
Well-Posedness of Nonlinear Estimation Problems
Then there exists a well-defined solution x in terms of stochastic differential equations
to (4.30) with v(t) considered as white noise provided that F̂1 and F̂2 can be written as
−1
F̂1 = F̂1 t, x1 , x3 , ẋ1 − σ(x1 , x3 )v, ẋ3 + F̂2;x
F̂
σ(x1 , x3 )v
(4.34a)
3 2;x1
F̂2 = F̂2 t, x1 , x3
(4.34b)
for some function σ(x1 , x3 ).
Proof: Differentiating (4.34b) with respect to time yields
F̂2;t + F̂2;x1 ẋ1 + F̂2;x3 ẋ3 = 0.
(4.35)
Since F̂2 is locally solvable for x3 , F2;x3 is invertible. This means that ẋ3 can be written
as
−1
(F̂2;t + F̂2;x1 ẋ1 ).
(4.36)
ẋ3 = −F̂2;x
3
(4.34b) can also be locally solved for x3 to give
x3 = R(t, x1 )
(4.37)
for some function R. Inserting this into (4.34a) gives
−1
−1
F̂1 t, x1 , R, ẋ1 − σ(x1 , R)v, −F̂2;x
(F̂2;t + F̂2;x1 ẋ1 ) + F̂2;x
F̂
σ(x1 , R)v . (4.38)
3
3 2;x1
The equation F̂1 = 0 now takes the form
−1
−1
F̂1 t, x1 , R, ẋ1 − σ(x1 , R)v, −F̂2;x
F̂ − F̂2;x
F̂
(ẋ1 − σ(x1 , R)v) = 0. (4.39)
3 2;t
3 2;x1
Since Corollary 2.1 is fulfilled, this equation can be solved for ẋ1 . Since −σ(x1 , R)v
enters the equations in the same way as ẋ1 , the solution takes the form
ẋ1 − σ(x1 , R)v = L(t, x1 )
(4.40)
for some function L. This can be interpreted as the stochastic differential equation
dx1 = L(t, x1 )dt + σ(x1 , R)dv
(4.41)
so x1 has a well-defined solution. A solution for x3 is then defined through (4.37).
If noise has been added to a DAE model using physical insight or for other reasons,
the theorem above gives conditions for the system to be well-posed using a transformed
version of the system. It may also be interesting to be able to see if the SDAE is wellposed already in the original equations. As discussed in the theorem above, the SDAE is
well-posed if the equations F̂1 = 0 and F̂2 = 0 take the form
−1
F̂1 t, x1 , x3 , ẋ1 − σ(x1 , x3 )v, ẋ3 + F̂2;x
F̂
σ(x1 , x3 )v = 0
(4.42a)
3 2;x1
F̂2 t, x1 , x3 = 0.
(4.42b)
In the original equations, this can typically be seen as adding noise according to
ẋ1 − σ(x1 , x3 )v
x
F
, 1 , t = 0.
−1
x3
ẋ3 + F̂2;x
F̂
σ(x
,
x
)v
1
3
3 2;x1
(4.43)
One common situation when it is easy to see how white noise can be added is a semiexplicit DAE (Brenan et al., 1996) with differential index 1. This is considered in the
following example.
4.5
73
Particle Filtering
Example 4.6: Noise modeling: semi-explicit index 1 DAE
Consider a semi-explicit DAE with differential index 1,
ẋa = f (xa , xb )
(4.44a)
0 = g(xa , xb ).
(4.44b)
Locally, xb can be solved from (4.44b), so these equations correspond to F̂1 = 0 and
F̂2 = 0 respectively. Noise can thus be added according to
ẋa = f (xa , xb ) + σ(xa , xb )v
0 = g(xa , xb ).
4.5
(4.45a)
(4.45b)
Particle Filtering
An important aspect of uncertain models is state estimation and prediction. For nonlinear systems this is a difficult problem (e.g., Ristic et al., 2004; Andrieu et al., 2004;
Schön, 2006). It is therefore necessary to resort to approximate methods. One approximate method for nonlinear state estimation is the particle filter (e.g., Gordon et al., 1993;
Doucet et al., 2001; Ristic et al., 2004). In this section we will discuss how particle filter
methods can be extended for use with SDAE models.
To be able to describe how existing particle filtering algorithms can be extended to
DAE systems, we will first briefly describe how particle filtering can be implemented
for state-space systems. Fore a more thorough treatment, see e.g., (Gordon et al., 1993;
Doucet et al., 2001; Ristic et al., 2004). Existing particle filtering methods may allow
other model structures than state-space systems, but we will limit the discussion here to
state-space systems since that is enough to extend particle filtering methods to SDAE
models.
Consider a nonlinear discrete-time state-space system,
x(tk+1 ) = f x(tk ), u(tk ), w(tk )
(4.46a)
y(tk ) = h x(tk ) + e(tk )
(4.46b)
where x is the state vector, u is a known input, y is a measured output, and w and e are
stochastic processes with known probability density functions. The particle filter is based
on estimating the probability density function of the state x(tk ), given the measurements
Z N = {u(t0 ), y(t0 ), ..., u(tN ), y(tN )}.
We are thus interested in computing the probability density function
p x(tk )|Z N .
(4.47)
(4.48)
74
4
Well-Posedness of Nonlinear Estimation Problems
Depending on if k < N , k = N , or k > N we will have a smoothing problem, filtering
problem, and prediction problem respectively. Here we will limit the discussion to the
filtering problem and the one-step-ahead prediction problem, that is we will have N = k
or N = k − 1.
Once (the estimate of) the probability density function has been computed, it can be
used to estimate the value of x(t). One possibility is to use the expected value of x(tk )
given Z N , another is to use the maximum a posteriori estimate, that is the x(tk ) that
maximizes p x(tk )|Z N .
In the particle filter, the probability density function (4.48), here with N = k − 1, is
approximated by a sum of generalized Dirac functions,
M
X
(i)
(i)
p x(tk )|Z k−1 ≈
qtk |tk−1 δ x(tk ) − xtk |tk−1 .
(4.49)
i=1
This means that the density function is approximated using M particles
(i)
(4.50)
(i)
(4.51)
{xtk |tk−1 }M
i=1
with associated weights,
{qtk |tk−1 }M
i=1 .
Since the approximation is made using Dirac functions, it is not an approximation at each
point x. Instead, the approximation holds for integrals of p. We can for example estimate
the mean value of x(tk ) as
E x(tk )|Z k−1 =
Z
M
X
(i)
(i)
x · p x(tk )|Z k−1 dx ≈
qtk |tk−1 xtk |tk−1 .
(4.52)
i=1
Now assume that a new measurement {y(tk ), u(tk )} is obtained. Using Bayes’s rule,
the probability density function p x(tk )|Z k−1 should be updated according to
p x(tk )|Z
k
p y(tk )|x(tk ) p x(tk )|Z k−1
=
.
p y(tk )|Z k−1
(4.53)
Since p y(tk )|Z k−1 does not depend on x, the particle filter updates its approximation
(i)
of the probability density function by updating the weights {qtk |tk−1 }M
i=1 according to
(i)
qtk |tk
(i)
(i)
p y(tk )|xtk |tk−1 qtk |tk−1
= PM
,
(j)
(j)
j=1 p y(tk )|xtk |tk−1 qtk |tk−1
i = 1, . . . , M.
For the state space description (4.46), we have that
(i)
(i)
p y(tk ) xtk |tk−1 = pe y(tk ) − h(xtk |tk−1 )
where pe is the probability density function of e(tk ).
(4.54)
(4.55)
4.5
75
Particle Filtering
After this step, called the measurement update, the resampling step takes place. The
resampling step redistributes the particles to avoid degeneration of the filter. It does not
introduce additional information (actually, information is lost). We will use so-called
sampling importance resampling. For other alternatives, see the references. The resampling step is in this case performed by replacing the M particles with M new particles.
This is done by drawing M particles with replacement from the old particles. The prob(i)
(i)
ability to draw particle i is proportional to its weight qtk |tk . The new particles xtk |tk are
thus chosen according to
(j)
(j)
(i)
(4.56)
Pr xtk |tk = xtk |tk−1 = qtk |tk i = 1, . . . , M.
The weights are changed to
(i)
qtk |tk =
1
M
i = 1, . . . , M
(4.57)
so that the approximation of the probability density function is, approximately, left unchanged.
After the resampling step, the time update step takes place. This means that x(tk+1 )
is predicted using available information about x(tk ). For the particle filter and the statespace model (4.46), this is done by drawing M independent samples of w(tk ), w(i) (tk ),
i = 1, . . . , M , according to its probability density function pw . The particles are then
updated according to
(i)
(i)
xtk+1 |tk = f xtk |tk , u(tk ), w(i) (tk ) ,
i = 1, . . . , M.
(4.58)
In general, this can be seen as drawing new particles according to their conditional distribution,
(i)
(i)
(4.59)
xtk+1 |tk ∼ p xtk+1 |tk xtk |tk , i = 1, . . . , M.
(i)
(i)
1
The weights are unchanged, qtk+1 |tk = qtk |tk = M
. Note that a more general version of
the time update equation is available, see the references. After this step, a new measurement is obtained and the filter is restarted from the measurement update step.
When starting a filter, the particles should be initialized according to available information about the initial value, x(t0 ). If the probability density function of x(t0 ) is px0 ,
the particles are initially chosen according to that distribution. We can write this as
(i)
xt0 |t−1 ∼ px0 (x0 ),
i = 1, . . . , M
(4.60)
and we get
1
, i = 1, . . . , M.
M
Summing up, we get the following particle filtering algorithm.
(i)
qt0 |t−1 =
(4.61)
1. Initialize the M particles,
(i)
xt0 |t−1 ∼ px0 (x0 ),
i = 1, . . . , M
(4.62)
76
4
and
(i)
qt0 |t−1 =
Well-Posedness of Nonlinear Estimation Problems
1
,
M
i = 1, . . . , M.
(4.63)
Set k := 0.
(i)
2. Measurement update: calculate weights {qtk |tk }M
i=1 according to
(i)
qtk |tk
(i)
(i)
p y(tk )|xtk |tk−1 qtk |tk−1
,
= PM
(j)
(j)
j=1 p y(tk )|xtk |tk−1 qtk |tk−1
i = 1, . . . , M.
3. Resampling: draw M particles, with replacement, according to
(j)
(j)
(i)
Pr xtk |tk = xtk |tk−1 = qtk |tk i = 1, . . . , M
and set
(i)
qtk+1 |tk =
1
M
i = 1, . . . , M.
4. Time update: predict new particles according to
(i)
(i)
xtk+1 |tk ∼ p xtk+1 |tk xtk |tk ,
i = 1, . . . , M.
(4.64)
(4.65)
(4.66)
(4.67)
5. Set k := k + 1 and iterate from step 2.
To examine how the implementation for DAE systems should be done, we consider
an SDAE in the form (4.6),
G ż(t), z(t), w(t), u(t) = 0
(4.68a)
y(tk ) = h z(tk ) + e(tk ).
(4.68b)
To be able to use methods for stochastic simulation with white noise inputs, we realize
the stochastic process w(t) as white noise v(t) filtered through a linear filter according to
what was discussed in Section 4.3. Following the discussion in Section 4.3, we can then
write the system as
F ẋ(t), x(t), v(t), u(t) = 0
(4.69a)
y(tk ) = h x(tk ) + e(tk ).
(4.69b)
We only consider SDAE models (4.69a) that fulfill the conditions of Theorem
theorem gives that we can write the system as
−1
F̂1 u(t), x1 , x3 , ẋ1 − σ(x1 , x3 )v, ẋ3 + F̂2;x
F̂
σ(x1 , x3 )v = 0
3 2;x1
F̂2 u(t), u̇(t), . . . , x1 , x3 = 0
x (t)
x(t) = Q 1
x3 (t)
y(tk ) = h x(tk ) + e(tk ).
4.1. The
(4.70a)
(4.70b)
(4.70c)
(4.70d)
4.6
77
Implementation Issues
for some permutation matrix Q.
Since F̂1 and F̂2 are the result of the transformations discussed in Section 2.2, F̂2 can
be locally solved for x3 ,
x3 = R u(t), u̇(t), . . . , x1 (t) .
(4.71)
After using (4.71) to eliminate x3 and ẋ3 in F̂1 , F̂1 can be solved for ẋ1 to give
ẋ1 = L(t, x1 ) + σ(x1 , R)v.
(4.72)
Combining (4.70)–(4.72) gives
ẋ1 = L(t, x1 ) + σ(x1 , R)v
x1 (tk )
y(tk ) = h Q
+ e(tk ).
R(u(tk ), u̇(tk ), . . . , x1 (tk ))
(4.73a)
(4.73b)
The state-space system (4.73) can be used to implement a particle filter for estimation
of x1 . After estimating x1 , estimates of x3 can be computed using (4.71).
Since it is typically not possible to solve for ẋ1 and x3 explicitly, we will discuss numerical implementation methods in the following section. Furthermore, the state equation
should be discretized. This can be done using for example a numerical solver for stochastic differential equations. The time update in step 4 in the particle filtering algorithm is
thus performed by solving (4.73a) for one time step. The measurement update in step 2
of the particle filtering algorithm is performed using the measurement equation (4.73b).
4.6
Implementation Issues
The exact transformation into the form (4.70), which is necessary to implement the particle filter may be difficult to compute in practice. It is also an issue how to solve these
equations numerically for ẋ1 and x3 . Therefore approximate implementations may be
considered. One way to do this is to use the type of DAE solver that is included in modeling environments for object-oriented modeling such as Dymola (Mattsson et al., 1998).
As discussed in Section 2.5, DAE solvers for component-based models compute an
approximation of the form
F̃1 (t, x1 , x3 , ẋ1 ) = 0
(4.74a)
F̂2 (t, x1 , x3 ) = 0,
(4.74b)
that is F̂1 and F̂2 with ẋ3 eliminated from F̂1 . This can be used to examine if a DAE with
a noise model satisfies the conditions of Theorem 4.1. The most straightforward way to
check if a given noise model is correct, is to examine if the transformed system is of the
form
F̃1 t, x1 , x3 , ẋ1 − σ(x1 , x3 )v = 0
(4.75a)
F̂2 (t, x1 , x3 ) = 0.
(4.75b)
78
4
Well-Posedness of Nonlinear Estimation Problems
If v appears in incorrect positions (so that the transformed system is not of the form
(4.75)), one way to handle the situation would be to remove v(t) from these incorrect
locations in F̃1 and F̂2 , and assumed that noise is added to the original equations so that
this is achieved.
The solvers can also be used for approximate implementation of particle filters for
DAE systems. The idea behind this is that the transformation to the form
ẋ1 = L(t, x1 ) + σ(x1 , R)v
(4.76a)
x3 = R(t, x1 )
(4.76b)
can be made by solving F̃1 and F̂2 numerically at each time step using a DAE solver.
This means that given values of x1 and v the solver can give ẋ1 and x3 . The state equation (4.76a) can then be used to estimate x1 , and x3 can be computed from (4.76b).
To summarize, the following procedure can be used when modeling noise in DAEs
and implementing a particle filter. First a DAE without noise is produced by writing down
equations, or from component-based modeling. This DAE is then entered into a DAE
solver to determine which variables that are states. Noise is then added to the original
equations according to physical insight and then the equations are transformed into F̃1
and F̂2 . Then incorrect noise terms are removed so that the equations are in the form
(4.75). The form (4.75) is then used to implement the particle filter by solving for ẋ1 and
x3 using the DAE solver.
4.7
Example: Dymola Assisted Modeling and Particle
Filtering
Figure 4.1: A pendulum
In this section we examine a DAE model of a pendulum. First noise is added, and
then a particle filter is implemented to estimate the internal variables of the pendulum.
This is a modified example from Brenan et al. (1996). As shown in Figure 4.1, z1
and z2 are the horizontal and vertical position of the pendulum. Furthermore, z3 and z4
are the respective velocities, z5 is the tension in the pendulum, the constant b represents
resistance caused by the air, g is the gravity constant, and L is the constant length of the
4.7
Example: Dymola Assisted Modeling and Particle Filtering
79
pendulum. The equations describing the pendulum are
ż1 = z3
(4.77a)
ż2 = z4
(4.77b)
ż3 = −z5 · z1 − b ·
ż4 = −z5 · z2 − b ·
0=
z12
+
z22
z32
z42
(4.77c)
−g
2
−L .
(4.77d)
(4.77e)
We will use the approximate methods discussed in Section 4.6, so the equations are
entered into the DAE solver in Dymola. The first step in the noise modeling is to let
Dymola select which variables that are states. There are several possible ways to select
states for these equations, but here z1 and z3 are selected. We thus have
 
z2
z1
(4.78)
, x3 = z4  .
x1 =
z3
z5
We can thus take F̂1 as
F̂1 =
ż1 − z3
ż3 − (−z5 · z1 − b · z32 )
(4.79)
corresponding to (4.77a) and (4.77c). White noise could thus be added to the states z1 and
z3 . We choose to add noise only to ż3 to model disturbances caused by e.g., turbulence.
(4.77a) and (4.77c) then take the form
ż1 = z3
(4.80a)
ż3 = −z5 · z1 − b ·
z32
+v
(4.80b)
where v is white noise. This corresponds to
0
σ=
1
(4.81)
in (4.34). The next step in the noise modeling is to transform these equations together
with the remaining noise-free equations into F̃1 and F̂2 in (4.75). Doing this reveals that
F̃1 , which is available as C code from Dymola, is of the desired form
F̃1 t, x1 , x3 , ẋ1 − σ(x1 , x2 )v ,
(4.82)
that is, the noise term only occurs in affine form and together with ẋ1 . However, F̂2
includes the noise term v which is not allowed. To solve this problem, occurrences of v
in F̂2 are deleted before it is used for particle filtering. Removing the noise from F2 can
typically be seen as adding noise in the original equations, but a user does not need to
consider the exact form of this. (For illustration, we will anyway discuss this below.)
Next, we implement a particle filter to estimate the internal variables of the system.
To generate data for the estimation experiment, the model is inserted into the Simulink
80
4
Well-Posedness of Nonlinear Estimation Problems
environment using the Dymola-Simulink interface available with Dymola. The purpose
of this experiment is not to demonstrate the performance of a filtering algorithm, but
rather to show how DAE models can be used in a direct way when constructing particle
filters. Therefore it is sufficient to use simulated data for the experiment. The constants
were chosen as L = 1, b = 0.05 and g = 9.81. Process noise was generated with the
Band-Limited White Noise block in Simulink with noise power 0.01. The initial values
of the states were z1 = 0.5 and z3 = −0.1. The measured variable is the tension in the
pendulum z5 ,
y(tk ) = z5 (tk ) + e(tk ).
(4.83)
Measurements with noise variance 0.1 was collected at the sampling interval 0.05 s.
After generating the data, a particle filter was implemented using the algorithm in
Section 4.5 to estimate the internal variables z1 , z2 , z3 , z4 , and z5 . Since the selected
states are z1 and z3 , these are the variables that are estimated directly by the particle filter.
The remaining variables are then computed by Dymola using F̂2 .
The particle filter was implemented in M ATLAB with the time updates being performed by simulating the model using the Dymola-Simulink interface. The initial particles were spread between z1 = 0.1 and z1 = 0.6 and between z3 = −0.2 and z3 = 0.2.
Only positive values of z1 were used since the symmetry in the system makes it impossible to distinguish between positive and negative z1 using only measurements of z5 . The
particle filter was tuned to use noise power 0.1 for the process noise and variance 0.2 for
the measurement noise to simulate the situation were the noise characteristics are not exactly known. A typical result of an estimation is shown in Figure 4.2 where an estimation
of z1 is plotted together with the true value.
0.6
0.4
z1 (m)
0.2
0
−0.2
−0.4
Estimated
True
−0.6
−0.8
0
0.5
1
1.5
Time (s)
2
2.5
3
Figure 4.2: Typical result of particle filtering.
To examine the reliability of the filtering algorithm, 100 Monte Carlo runs were made.
4.7
81
Example: Dymola Assisted Modeling and Particle Filtering
Then the RMSE value was calculated according to
v
u
M
u 1 X
2
RMSE(t) = t
x(t) − x̂j (t)
M j=1
(4.84)
where M is the number of runs, here M = 100, x(t) is the true state value and x̂j (t) is
the estimated state value in run j. The result is shown in Figure 4.3. The estimation error
0.25
z1
z3
RMSE
0.2
0.15
0.1
0.05
0
0
0.5
1
1.5
Time (s)
2
2.5
3
Figure 4.3: RMSE for the estimations of z1 and z3 for 100 Monte Carlo runs.
in the velocity z3 is larger when the pendulum changes direction, which could mean that
it is more difficult to estimate the velocity there.
Noise modeling details
When adding noise to a DAE, a user can only add noise so that it enters through a function
σ. This was done in equation (4.80) above. However, noise must also be added according
−1
to the term F2;x
F̂
σ(x1 , x3 )v in (4.43) to make all variables well-defined (otherwise
3 2;x1
the conditions of Theorem 4.1 will not be satisifed).
To compute F̂2 , we consider (4.77).
0 = z12 + z22 − L2 .
(4.85)
Differentiating (4.85) with respect to time gives
0 = 2z1 ż1 + 2z2 ż2 .
(4.86)
Inserting (4.77a) and (4.77b) gives
0 = 2z1 z3 + 2z2 z4
(4.87)
82
4
Well-Posedness of Nonlinear Estimation Problems
which after differentiation gives
0 = 2ż1 z3 + 2z1 ż3 + 2ż2 z4 + 2z2 ż4 .
(4.88)
Inserting the expressions for the derivatives gives
0 = 2z32 + 2z1 (−z5 · z1 − bz32 ) + 2z42 + 2z2 (−z5 · z2 − bz42 − g).
(4.89)
The equations (4.85), (4.87), and (4.89) together define one possible selection of F̂2 .
These can be used to compute
 
0
0
−1
−1
 z1 
F2;x
F̂
σ(x
,
x
)v
=
F
=
(4.90)
F̂
2;x
1
3
2;x
1
1
2;x
z
3
3
2
1
∗
where the last term ∗ is unimportant since ż5 does not occur in the equations. This tells
us that noise should be added to ż4 according to
ż4 +
z1
v = −z5 · z2 − b · z42 − g
z2
(4.91)
to satisfy the conditions of Theorem 4.1.
4.8
Parameter Estimation
In the parameter estimation problem, we are interested in estimating unknown parameters θ in a nonlinear SDAE with sampled measurements,
F ẋ(t), x(t), u(t), v(t), θ = 0
(4.92a)
y(tk ) = h(x(tk ), θ) + e(tk ).
(4.92b)
As discussed in Section 4.3, the white noise v(t) is typically the result of physical noise
modeling, where low bandwidth noise is realized as white noise filtered through a linear
filter. As in Chapter 3, we denote the measurements by
Z N = {u(t0 ), y(t0 ), ..., u(tN ), y(tN )}.
(4.93)
The parameters θ belong to the domain DM ⊆ Rnθ , θ ∈ DM .
To guarantee that the model is well-defined, it will be assumed that it fulfills the
conditions of Theorem 4.1 so that it can be written as
−1
F̂1 u, θ, x1 , x3 , ẋ1 − σ(x1 , x3 )v, ẋ3 + F̂2;x
F̂
σ(x1 , x3 )v = 0
(4.94a)
3 2;x1
F̂2 u, θ, x1 , x3 = 0
(4.94b)
y(tk ) = h x1 (tk ), x3 (tk ), θ + e(tk )
(4.94c)
for all θ ∈ DM . As discussed previously in the chapter, this means that a particle filter
can be implemented (for each value of θ). The particle filter gives two possibilities to
4.8
83
Parameter Estimation
estimate the unknown parameters, the maximum likelihood method and the prediction
error method (see Chapter 3).
The maximum likelihood method using particle filters is discussed by Andrieu et al.
(2004). Here the probability density function of the output, fy (θ, Z N ) is estimated using the particle filter. The parameters are thus estimated by maximizing the likelihood
function,
θ̂ = arg max fy (θ, Z N )
(4.95)
θ
or the log-likelihood function,
θ̂ = arg max log fy (θ, Z N ).
(4.96)
θ
In can be noted that even though we have modeled all uncertainties as stochastic processes (4.92a), it is not straightforward to compute the likelihood function since there
are several tuning parameters in the particle filter. For example, the number of particles
and the resampling technique must be specified. Another issue, which is discussed by
Andrieu et al. (2004), is that the particle filter itself is stochastic. This means that the
estimate of the likelihood function fy (θ, Z N ) will not be a smooth function, the estimate
will not even be the same if computed two times with the same value of θ. A solution to
this problem discussed by Andrieu et al. (2004) is to use the same noise realization each
time the likelihood function is estimated. This will lead to a smooth estimate.
To use the prediction error method, it is necessary to compute the models prediction
of y(tk ) given Z k−1 , i.e., ŷ(tk |tk−1 , θ). The predictions can for example be computed
using the particle filter. Using the predictions from the particle filter (or another method),
the parameters can be estimated using
VN (θ, Z N ) =
N
T
1 X
y(tk ) − ŷ(tk |tk−1 , θ) Λ−1 y(tk ) − ŷ(tk |tk−1 , θ)
N
(4.97a)
k=1
θ̂ = arg min VN (θ, Z N )
(4.97b)
θ
where Λ weighs together the relative importance of the measurements. As for the maximum likelihood method, the result will depend on the tuning of the particle filter and the
function VN will not be smooth since the particle filter itself is stochastic. As before, the
latter problem could be solved by using a fixed noise realization for all values of θ.
For the output-error case,
F ẋ(t), x(t), u(t), θ = 0
(4.98a)
y(tk ) = h(x(tk ), θ) + e(tk ),
(4.98b)
the prediction error method is simplified, since the predictions ŷ(tk |tk−1 , θ) then are obtained by simulating the system. This approach is discussed thoroughly by Schittkowski
(2002).
Example 4.7: Prediction error method
To illustrate the prediction error method for DAE models using particle filters, the pendulum model of Section 4.7 is considered. We have computed the criterion function VN
84
4
Well-Posedness of Nonlinear Estimation Problems
in (4.97a) for the pendulum model in Section 4.7 for different values of the parameter L.
The setting is the same as in Section 4.7, except for the measurement noise e(tk ) in
y(tk ) = z5 (tk ) + e(tk )
(4.99)
which has variance 0.01 in this example. The measurements Z N have been obtained by
simulation of the model. Measurements have been collected with sampling interval 0.05 s
for the duration of 1 s. Since we have a scalar output, the weighting matrix Λ is set to 1.
A straightforward implementation of the particle filter for computation of the predictions
ŷ(tk |tk−1 , θ) results in the criterion function VN in Figure 4.4a. As can be seen in the
30
10
25
8
VN
VN
20
6
15
4
10
2
0.9
0.95
1
1.05
1.1
L
(a) Different noise realizations in the particle
filter for each value of L.
5
0.9
0.95
1
1.05
1.1
L
(b) The same noise realization in the particle
filter for each value of L.
Figure 4.4: The value of the criterion function VN for different values of the parameter L. The true value is L = 1.
figure, VN has its minimum near the true value L = 1. However, VN is not very smooth
which means that it would be difficult to find its minimum. To make the situation better,
we compute VN using the same noise realization in the particle filter for each value of L.
The result is shown in Figure 4.4b. In this case, there is a more distinct minimum at the
true value L = 1. Because of the approximations involved in the particle filter, VN is not
completely smooth in this case either.
If the initial condition x(0) is unknown, it should normally be estimated along with
the parameters. From (4.94) we get that x1 is the state variable, and x3 is a function
of x1 . This means that only x1 (0) should be parameterized and estimated along with the
parameters, while x3 (0) is computed from (4.94b). However, since the particle filter can
work with the distribution of x1 (0) other approaches, such as spreading the particles in
an area where the x1 (0) is known to lie, are possible.
There are also other parameter estimation methods available than the well-established
prediction-error and maximum likelihood methods. One method which has been sug-
4.9
Conclusions
gested is to extend the internal variables x with the unknown parameters θ,
x
z=
θ
85
(4.100)
and then estimate z using an estimation method like the extended Kalman filter or the
particle filter (e.g., Ljung, 1979; Schön and Gustafsson, 2003; Andrieu et al., 2004). Since
θ is constant, a direct implementation of the particle filter will not work (Andrieu et al.,
2004, Section IV.A). Alternative methods are discussed by Andrieu et al. (2004).
4.9
Conclusions
We have presented a theoretical basis for introduction of noise processes in DAE models.
The exact conditions that this gives can be hard to use in practice, for example since it
requires rank tests. Therefore, an approximate solution was proposed. This solution uses
the kind of DAE solvers that are included in modeling environments for object-oriented
modeling. These solvers produce an approximation of the transformation that is necessary
to include noise in an appropriate way.
It was also discussed how particle filtering can be implemented for DAE models. An
example which shows that it is possible to implement a particle filter using a DAE model
was presented. The results were similar to what could be expected from an implementation using a regular state-space model.
It was also discussed how estimation of unknown parameters can be performed using
the prediction error and maximum likelihood methods. Provided that the model structure
is well-defined, the particle filter can be used in the implementation.
Further research issues include to examine if it is possible to implement other estimation methods such as extended Kalman filtering for DAE models. Another research issue,
which also is of interest for other model structures than DAEs, is how the particle filter
should be tuned to provide smooth estimates of the probability density function fy (θ, Z N )
and the prediction error criterion function VN (θ, Z N ) for parameter estimation.
86
4
Well-Posedness of Nonlinear Estimation Problems
5
Identifiability and Observability for
DAEs Based on Kunkel and
Mehrmann
In this chapter we will discuss how rank tests can be used to examine local identifiability
and observability for differential-algebraic equations.
5.1
Introduction
For state-space models, it is common practice to use rank tests to examine observability
and identifiability. Consider for example the state-space model
ẋ(t) = Ax(t) + Bu(t)
(5.1a)
y(t) = Cx(t)
(5.1b)
with x ∈ Rn . The basic way to examine observability for this system is to check if the
matrix


C
 CA 


(5.2)
 .. 
 . 
CAn−1
has full column rank, which is a necessary and sufficient condition (e.g., Rugh, 1996).
Similarly, a sufficient condition for local identifiability and local weak observability of a
nonlinear state-space system
ẋ(t) = f x(t), θ
(5.3a)
y(t) = h x(t), θ
(5.3b)
87
88
5
Identifiability and Observability for DAEs Based on Kunkel and Mehrmann
is existence of an integer k such that the matrix


hx,θ x(t), θ
h(1) x(t), θ
 x,θ



..


.


(k)
hx,θ x(t), θ
has full rank. As before, we use the notation
(k)
∂
∂
∂
...
hx,θ = ∂x1 . . . ∂xn
∂θ1
x
∂
∂θnθ
(5.4)
dk
.
h
x(t),
θ
dtk
(5.5)
This test is for example discussed by Walter (1982, Section 3.2.1). In this chapter, we
will discuss how such methods can be extended to DAE models. To do this, we will use
the results by Kunkel and Mehrmann (2001) that were reviewed in Section 2.2. Since
DAE models are more difficult to handle, the results will be more complicated than for
state-space systems.
We will also show that these results can be used to derive and extend standard observability and identifiability results for state-space models.
5.2
Identifiability
Consider the nonlinear DAE
F ẋ(t), x(t), θ, u(t), t = 0
y(t) = h x(t) .
(5.6a)
(5.6b)
According to the definition in Section 3.4, this DAE is locally identifiable at θ0 , x0 for the
input u(t) if there exists a neighborhood V of θ0 for which
θ∈V
(5.7)
⇒ θ = θ0
y(θ0 , t) = y(θ, t) for all t
where y(θ̄, t) is the output y of (5.6) with the input u(t), θ = θ̄, and the consistent initial
condition x0 . The system is locally identifiable if it is locally identifiable at all θ0 ∈ DM .
Identifiability for nonlinear DAE models has not been treated much in literature, one
reference being Ljung and Glad (1994). For state-space models, the problem is well
treated. See, e.g., the book by Walter (1982).
As discussed previously, identifiability for nonlinear systems is closely related to observability. This can be seen by noting that identifiability of θ in the DAE model
F ẋ(t), x(t), θ, u(t), t = 0
(5.8a)
y(t) = h x(t)
(5.8b)
can bee seen as observability of the (constant) variable θ(t) in the model
F ẋ(t), x(t), θ(t), u(t), t = 0
(5.9a)
θ̇(t) = 0
(5.9b)
y(t) = h x(t) .
(5.9c)
5.3
5.3
Observability Tests Based on Kunkel and Mehrmann
89
Observability Tests Based on Kunkel and
Mehrmann
We consider a nonlinear DAE,
G ẋ(t), x(t), u(t), t = 0
y(t) = h x(t)
(5.10a)
(5.10b)
where x(t) ∈ Rnx are the internal variables, u(t) external inputs, and y(t) a measured
output. The idea when examining observability for the system (5.10), is that if the system
is observable, then enough information should be contained in the equations to compute
x(t) when u(t) and y(t) are known signals. This means that (5.10a) and (5.10b) both
should be used as equations that give information about x(t). Collecting the equations
gives the extended DAE
G ẋ(t), x(t), u(t),
t = 0.
(5.11)
y(t) − h x(t)
{z
}
|
F
What needs to be done is to examine if x(t) can be solved uniquely from (5.11). Locally, the uniqueness of the solutions can be examined using the method by Kunkel
and Mehrmann (2001) that is given in Theorem 2.2. Doing this results in the following theorem, which as far as we know is a novel application of the theory of Kunkel and
Mehrmann.
Theorem 5.1
Assume that the extended DAE (5.11) fulfills the conditions of Theorem 2.2 for some µ, a,
d, and v and the solution x0 (t). Then the original DAE (5.10) is locally weakly observable
at x0 (t) if and only if a = nx where nx is the dimension of x.
Note that u(t) and y(t) should be seen as time-dependent signals and thus be included
in the general time-dependency t in Theorem 2.2.
Proof: Assume that a = nx . Then, according to Theorem 2.2, the solution to the extended DAE (5.11) is locally described by
x3 (t) = R(t)
(5.12)
and x1 and x2 have dimension 0. Since x3 then describes the solution for x(t), these variables are (locally) determined by the extended DAE. This means that if y(t) is replaced by
the output from a similar system with the solution x0 (t), then there is a neighborhood of
x0 (t) where x is uniquely determined by (5.11) so the original DAE is locally observable.
Now assume that a < nx . Then, according to Theorem 2.2, the solution to the extended DAE (5.11) is locally described by
ẋ1 (t) = L t, x1 (t), x2 (t), ẋ2 (t)
(5.13a)
x3 (t) = R t, x1 (t), x2 (t)
(5.13b)
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Identifiability and Observability for DAEs Based on Kunkel and Mehrmann
where the dimension of at least one of x1 and x2 is greater than zero. According to
Proposition 2.1 there is a neighborhood where the initial condition of these variables
can be chosen freely. This means that x(t) has an undetermined initial condition or is a
parameter that can be varied freely without changing the output y(t). This means that the
original system is not locally weakly observable.
Finally, the case a > nx cannot occur since a is the difference between the ranks of
the matrices in (2.46) and (2.48).
5.4
Identifiability Tests based on Kunkel and
Mehrmann
We consider a nonlinear DAE with unknown parameters,
G ż(t), z(t), θ, u(t), t = 0
y(t) = h z(t), θ
(5.14a)
(5.14b)
where z(t) ∈ Rnz are the internal variables, θ ∈ Rnθ the unknown constant parameters,
u(t) external inputs, and y(t) a measured output. As discussed in Section 5.2, identifiability of the DAE (5.14) can be seen as adding the equation
θ̇(t) = 0
(5.15)
and examining if
x(t) =
z(t)
θ(t)
(5.16)
is observable in the new system. We can thus use the results on observability from the
previous section to examine identifiability. The extended DAE then takes the form


G ż(t), z(t), θ(t), u(t),
t
 = 0.

y(t) − h z(t), θ
(5.17)
θ̇(t)
|
{z
}
F
Applying the results in the previous section results in the following theorem.
Theorem 5.2
Assume that the extended DAE (5.17) fulfills the conditions of Theorem 2.2 for some µ, a,
d, and v with
z(t)
x(t) =
(5.18)
θ(t)
at z0 (t), θ0 . Then the original DAE (5.14) is locally identifiable and locally weakly observable at z0 (t), θ0 if and only if a = nz + nθ where nz is the dimension of z and nθ is
the dimension of θ.
5.4
Identifiability Tests based on Kunkel and Mehrmann
Proof: The result follows directly from Theorem 5.1 with
z(t)
x(t) =
.
θ(t)
91
(5.19)
If it is known beforehand that the system is observable if all parameter values θ are
known, then it is possible to examine local identifiability without having to treat observability at the same time. This is described in the following corollary.
Corollary 5.1
Assume that the original DAE (5.14) is locally weakly observable if θ is known and that
the extended DAE (5.17) fulfills the conditions of Theorem 2.2 for some µ, a, d, and v
with
z(t)
x(t) =
(5.20)
θ(t)
at θ0 . Then the original DAE (5.14) is locally identifiable at θ0 if and only if a = nz + nθ
where nz is the dimension of z and nθ is the dimension of θ.
Proof: If a = nz + nθ , the system is clearly both locally weakly observable and locally
identifiable according to Theorem 5.2. If a < nz + nθ , the solution of the extended DAE
is locally described by
ẋ1 (t) = L t, x1 (t), x2 (t), ẋ2 (t)
(5.21a)
x3 (t) = R t, x1 (t), x2 (t)
(5.21b)
where the initial value of x1 and x2 can be chosen freely in some neighborhood and the
dimension of at least one of these variables is greater than zero. Since z(t) is locally
weakly observable, it must be part of x3 (t). At least part of θ is thus included in x1 (t)
and/or x2 (t), so it is not locally identifiable.
Example 5.1: Identifiability based on Kunkel and Mehrmann
Consider the DAE
θż(t) − u(t)
= 0.
y(t) − z(t)
To examine identifiability using Theorem 5.2, we consider the extended DAE


θ(t)ż(t) − u(t)
 y(t) − z(t)  = 0.
θ̇(t)
|
{z
}
(5.22)
(5.23)
F
Let
x(t) =
z(t)
.
θ(t)
(5.24)
To examine if the conditions of Theorem 2.2 are satisfied, we must verify that Property 2.1
holds. We will first verify that if holds for µ = 2. Not that the number of variables are
n = 2 and the number of equations are m = 3. The steps of Property 2.1 can be verified
as follows.
92
5
Identifiability and Observability for DAEs Based on Kunkel and Mehrmann
1. We have that

θ(t)ż(t) − u(t)


y(t) − z(t)




θ̇(t)


 


F
θ̇(t)
ż(t)
+
θ(t)z̈(t)
−
u̇(t)


d
.
F=
ẏ(t)
−
ż(t)
F2 =  dt


d2


θ̈(t)


dt2 F
θ̈(t)ż(t) + 2θ̇(t)z̈(t) + θ(t)z (3) (t) − ü(t)




ÿ(t) − z̈(t)
(3)
θ (t)

(5.25)
All variables except the time t are determined by the equations, so L2 ⊆ R9 forms
a manifold of dimension 1. This gives
r = 9 − 1 = 8.
(5.26)
2. We have

F2;x,ẋ,ẍ,x(3)
0
1

0

0

=
0
0

0

0
0
ż
0
0
z̈
0
0
θ 0
0 0
0 1
θ̇ ż
1 0
0 0
θ̈ 2z̈
0 0
0 0
z (3)
0
0
0
0
0
θ
0
0
2θ̇
1
0
0
0
0
0
0
1
ż
0
0
0
0
0
0
0
0
θ
0
0

0
0

0

0

0

0

0

0
1
(5.27)
which gives that
rank F2;x,ẋ,ẍ,x(3) = 8
(5.28)
provided that, for example, θ 6= 0 and z̈ 6= 0. This is consistent with r = 8.
3. Since corank F2;x,ẋ,ẍ,,x(3) = 1 and

F1;x,ẋ,ẍ
0
1

0
=
0

0
0
ż
0
0
z̈
0
0
θ
0
0
θ̇
1
0
0
0
1
ż
0
0
0
0
0
θ
0
0

0
0

0

0

0
1
has full row rank (corank F1;x,ẋ,ẍ = 0), we have that v = 1.
(5.29)
5.5
93
Application to State-Space Models
4. We have

F2;ẋ,ẍ,x(3)
θ
0

0

θ̇

=
1
0

θ̈

0
0
0
0
1
ż
0
0
2z̈
0
0
0
0
0
θ
0
0
2θ̇
1
0
0
0
0
0
0
1
ż
0
0
0
0
0
0
0
0
θ
0
0

0
0

0

0

0

0

0

0
1
(5.30)
which gives
a = r − rank F2;ẋ,ẍ,x(3) = 8 − 6 = 2.
(5.31)
This gives that T2 is of size zero.
5. Since T2 is of size zero, we have that
rank Fẋ T2 = 0.
(5.32)
m − a − v = 3 − 2 − 1 = 0,
(5.33)
d = 0.
(5.34)
Since
we have a well-defined
Corresponding calculations give that Property 2.1 is fulfilled also with µ = 3, a = 2,
d = 0, and v = 1. This gives that Theorem 2.2 is fulfilled for the extended DAE. Since
a = nz + nθ , Theorem 5.2 gives that the model is locally identifiable and locally weakly
observable.
The calculations in the example involve quite large matrices, but the fact that the smaller
matrices are submatrices of F2;x,ẋ,ẍ,x(3) makes the computations easier.
5.5
Application to State-Space Models
As was mentioned in the beginning of the chapter, the method discussed here can be seen
as a generalization of rank tests that are used to examine identifiability and observability
of state-space models. To make this connection clear, we present results from state-space
theory in this section and prove them with the general theory of Section 5.3 and 5.4. We
will also see that the methods discussed here will make it possible to show that results that
usually are referred to as necessary conditions, under certain conditions are necessary and
sufficient.
First consider a linear state-space model,
ż(t) = Az(t)
(5.35a)
y(t) = Cz(t)
(5.35b)
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5
Identifiability and Observability for DAEs Based on Kunkel and Mehrmann
where z ∈ Rnz , nz ≥ 1, and y ∈ Rny , ny ≥ 1. We will show that observability of this
model is equivalent to the basic observability test (5.2). To examine observability, we will
use Theorem 5.1. The extended DAE is
ż(t) − Az(t)
= 0,
(5.36)
Cz(t) − y(t)
{z
}
|
F
which gives the (nz + ny )(µ + 1) × (µ + 2)nz matrix Fµ;x,ẋ,...,x(µ+1) (with x(t) = z(t))

Fµ;x,ẋ,...,x(µ+1)




=



−A
I
0
···
C
0
0
..
.
−A
0
..
.
0
0
···
0
0
−A
C

0
.. 
.


.
0


I
0
(5.37)
Note that m = nz + ny and n = nz . By multiplying this matrix from the right with the
full rank matrix


I
0
0 ··· 0
 A
I
0
0


 2
.. 
 A

A
I
.
(5.38)


 .

.
.
..
. . 0
 ..
Aµ+1
Aµ
···
and then from the left with the full rank matrix

I
0
0

0
I
0


0
0
I

 −C
0
0


0
0
0

 −CA
0
−C


..

.
0
0
0
I
0
0
−CA
0
µ−1
0
−CA
µ−2
Fµ;x,ẋ,...,x(µ+1) can be brought into the form

0
I 0
 C
0 0

 0
0
I

 CA 0 0

 0
0 0

 ..
.. ..
 .
. .

 0
0 0
CAµ 0 0
0
0
0
0
I
A
I
0
0
0
0
I
0
0
0
0
0
0
I
..
···
···
..
···
···
···
.
0
0

0
0

0

0

.
0

.. 
.

I
0
.
···

0
0

0

0

0

0

.. 
.
(5.39)
I
(5.40)
5.5
95
Application to State-Space Models
By row permutations, this matrix can be written as


C
0
 CA 0


 ..

.
 .
0


CAµ 0
0
I
(5.41)
Since multiplication with full rank matrices and row permutations do not change the rank
of a matrix, Fµ;x,ẋ,...,x(µ+1) has full column rank if and only if


C
 CA 


(5.42)
 ..  .
 . 
CAµ
has full rank.
It must now be shown that Property 2.1 is fulfilled for µ = nz and the value of a must
be determined to see if the model is locally weakly observable.
Let µ = nz and assume that Fµ;x,ẋ,...,x(µ+1) has full column rank so that r = (µ +
2)nz . According to the Cayley-Hamilton theorem, Fµ−1;x,ẋ,...,x(µ) also has full column
rank, so
v = (nz + ny )(µ + 1) − (µ + 2)ny − (nz + ny )µ − (µ + 1)nz = ny . (5.43)
Furthermore, a = nz since Fµ;x,ẋ,...,x(µ+1) and Fµ;ẋ,...,x(µ+1) have full column rank. This
gives that T2 is the empty matrix, so d = 0. Also, m − a − v = 0, so Property 2.1 is
satisfied with a = nz for µ = nz .
Now assume that Fµ;x,ẋ,...,x(µ+1) does not have full rank, so that r = (µ + 2)nz − ∆r
for some ∆r > 0. According to the Cayley-Hamilton theorem we also have
rank Fµ−1;x,ẋ,...,x(µ) = (µ + 1)nz − ∆r,
(5.44)
so
v = (nz +ny )(µ+1)−(µ+2)nz +∆r − (nz +ny )µ−(µ+1)nz +∆r = ny . (5.45)
Now, a = nz − ∆r since Fµ;ẋ,...,x(µ+1) has full column rank by construction. Also,
d = ∆r since Fẋ has full column rank by construction. This gives that m − a − v = ∆r
so Property 2.1 is satisfied.
The above discussion also holds for µ = nz + 1 according to the Cayley-Hamilton
theorem. This gives that the conditions of Theorem 2.2 are satisfied, so Corollary 5.1
gives that the model is locally weakly observable if and only if


C
 CA 


(5.46)
 .. 
 . 
CAnz
96
5
Identifiability and Observability for DAEs Based on Kunkel and Mehrmann
has full column rank. According to the Cayley-Hamilton theorem, this is equivalent to
full rank of the matrix


C
 CA 


(5.47)


..


.
CAnz −1
Full rank of this matrix is a standard observability criterion for linear state-space systems.
If the model has multiple outputs (ny ≥ 2), it may be sufficient to consider a matrix
with fewer rows. This is discussed below in connection with the so-called observability
indices. Also note that for linear model, local weak observability is equivalent to global
observability since all equations involved are linear.
It is well known that full rank of the matrix (5.47) is a necessary and sufficient condition for observability of a linear state-space model. For nonlinear state-space models the
situation is not as simple as for the linear case. While similar rank tests exist, they only
give sufficient conditions for observability. The following theorem shows how the method
that is discussed in this chapter not only reduces to a standard rank test for observability
of nonlinear state-space models, but also shows what conditions that need to be satisfied
to make it a necessary and sufficient condition.
(k)
In the theorem, we use the notation hz z(t) for the partial derivatives with respect
to z of the k:th time derivative of the function h z(t) ,
dk
∂
∂
(k)
hz z(t) = ∂z1 . . . ∂znz
h z(t) .
(5.48)
dtk
Note that for the state-space model
ż(t) = f z(t)
y(t) = h z(t) ,
the time derivatives can be recursively defined by
h(0) z(t) = h z(t)
h(i+1) z(t) = h(i)
z z(t) f z(t) .
(5.49a)
(5.49b)
(5.50a)
(5.50b)
Theorem 5.3
The nonlinear state-space model
ż(t) = f z(t)
y(t) = h z(t)
(5.51a)
(5.51b)
with z ∈ Rnz , nz ≥ 1, and y ∈ Rny , ny ≥ 1, is locally weakly observable if and only if
the matrix


hz z(t)
 h(1) z(t) 
 z



..


(5.52)
.


 (µ)

 hz z(t) 
(µ+1)
hz
z(t)
5.5
97
Application to State-Space Models
(µ)
has full column rank if µ is chosen so that the last two block rows, hz
(µ+1)
hz
z(t) , do not add column rank.
z(t) and
The condition of this theorem is typically referred to as a sufficient condition for observability (e.g., Nijmeijer and van der Schaft, 1990; Isidori, 1989). This theorem extends
the standard sufficient condition to a necessary and sufficient condition.
Proof: The extended DAE is
ż(t) − f z(t)
= 0.
h z(t) − y(t)
|
{z
}
(5.53)
F
Note that the time derivatives of f z(t) can be defined recursively by
f (0) z(t) = f z(t)
f (i+1) z(t) = fz(i) z(t) f z(t) .
(5.54a)
(5.54b)
This gives the (nz + ny )(µ + 1) × (µ + 2)nz matrix Fµ;x,ẋ,...,x(µ+1) (with x(t) = z(t))
fz z(t)

 hz z(t)

 (1)
 fz z(t)


=  h(1) z(t)
 z

..

.

 (µ)
fz z(t)
(µ)
hz z(t)

Fµ;x,ẋ,...,x(µ+1)
I
0
0
0
0
I
0
0
0
0
0
0
···
..
0
.
···
···
0
0
0

0
.. 
.

.. 
.

.. 
.
.


0


I
0
(5.55)
We have that m = nz + ny and n = nz . Through column operations that do not change
the rank, this matrix can be brought into the form

0

 hz z(t)



0

 (1)
 hz z(t)


..

.


0
(µ)
hz z(t)
···
I
0
0
0
0
I
0
0
..
0
0
0
0
···
···
0
.
0
0
0

0
.. 
.

.. 
.

..  .
.


0

I
0
(5.56)
98
5
Identifiability and Observability for DAEs Based on Kunkel and Mehrmann
This matrix has full column rank if and only if the matrix


hz z(t)
 h(1) z(t) 
 z



..


.


 (µ−1)

h z
z(t) 
(µ)
hz z(t)
(5.57)
has full column rank.
It must now be examined if Property 2.1 is fulfilled and the value of a must be determined to see if the model is locally weakly observable. Let µ be selected so that the
(µ)
block row hz z(t) in (5.57) does not add rank to (5.57). Such a µ always exists since
the maximum rank of (5.57) is nz .
First assume that Fµ;x,ẋ,...,x(µ+1) has full column rank so that r = (µ + 2)nz . Since
(µ)
the block row hz z(t) in (5.57) does not add column rank, Fµ−1;x,ẋ,...,x(µ) also has
full column rank, so
v = (nz + ny )(µ + 1) − (µ + 2)ny − (nz + ny )µ − (µ + 1)nz = ny .
(5.58)
Furthermore, a = nz since Fµ;x,ẋ,...,x(µ+1) and Fµ;ẋ,...,x(µ+1) have full column rank. This
gives that T2 is the empty matrix, so d = 0. Also, m − a − v = 0, so Property 2.1 is
satisfied with a = nz .
Now assume that Fµ;x,ẋ,...,x(µ+1) does not have full rank, so that r = (µ + 2)nz − ∆r
for some ∆r > 0. Since the last block row in (5.57) does not add column rank, we have
rank Fµ−1;x,ẋ,...,x(µ) = (µ + 1)nz − ∆r,
(5.59)
so
v = (nz +ny )(µ+1)−(µ+2)nz +∆r − (nz +ny )µ−(µ+1)nz +∆r = ny . (5.60)
Now, a = nz − ∆r since Fnz ;ẋ,...,x(nz +1) has full column rank by construction. Also,
d = ∆r since Fẋ has full column rank by construction. Since m − a − v = ∆r we have
that Property 2.1 is satisfied with a = nz − ∆r.
The above discussion also holds for µ replaced by µ + 1 since the last block row
(µ+1)
hz
z(t) also does not add column rank. This gives that the conditions of Theorem 2.2 are satisfied, so Corollary 5.1 gives that the system is locally weakly observable
if and only if (5.52) has full column rank.
The next result shows how the methods discussed here also can be reduced to a rank
test for identifiability of state-space systems. This leads to a version of the identifiability test in Section 3.2.1 in Walter (1982) (use of the implicit function theorem to examine identifiability) if the so-called exhaustive summary is taken as the derivatives of the
output. Here we show that it can be taken as a necessary and sufficient condition with
appropriately selected matrix dimensions.
5.6
99
Other Insights Using Kunkel’s and Mehrmann’s Theory
Corollary 5.2
The nonlinear state-space system
ż(t) = f z(t), θ
(5.61a)
y(t) = h z(t), θ
(5.61b)
with z ∈ Rnz , nz ≥ 1, θ ∈ Rnθ , nθ ≥ 1, and y ∈ Rny , ny ≥ 1, is locally identifiable
and locally weakly observable if and only if the matrix

hz,θ z(t), θ
 h(1) z(t), θ 
 z,θ



..


.



 (µ)
 hz,θ z(t), θ 
(µ+1)
z(t), θ
hz,θ

(5.62)
(µ)
has full column rank, if µ is chosen so that the last two block rows, hz,θ z(t), θ and
(µ+1)
z(t), θ , do not add column rank.
hz,θ
Proof: The problem can be seen as examining observability of the system
ż(t) = f z(t), θ(t)
(5.63)
θ̇(t) = 0
(5.64)
y(t) = h z(t), θ .
(5.65)
Applying Theorem 5.3, we directly get that this system is locally weakly observable if
and only if the matrix (5.62) has full column rank.
5.6
Other Insights Using Kunkel’s and Mehrmann’s
Theory
In this section we will discuss how observability indices and zero dynamics can be discussed using the theory for DAE models by Kunkel and Mehrmann (2001).
5.6.1
Observability Indices
As discussed above, a sufficient (and necessary under certain conditions) condition for
local weak observability of the state-space system
ẋ(t) = f x(t)
y(t) = h x(t)
(5.66a)
(5.66b)
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5
Identifiability and Observability for DAEs Based on Kunkel and Mehrmann
is full rank of the matrix

hx x(t)
(1)
hx x(t)
..
.








.


 (nx −1)

h x
x(t) 
(n )
hx x x(t)
(5.67)
where nx is the dimension of x. This means that all outputs y are differentiated nx times
to prove identifiability. However, if y and h are vector valued, it may be sufficient to
examine a smaller matrix to conclude that the system is observable. In other words, it
may not be necessary to differentiate all outputs nx times. To see this, assume that there
are ny outputs and let the i:th time derivative of output j be denoted
(i)
hj x(t) .
(5.68)
The partial derivatives of this time derivative with respect to the states x is then denoted
(i)
hj;x x(t) .
(5.69)
The idea is now that if it is possible to find integers σ1 , . . . , σny (σk ≤ nx ) such that the
matrix


h1;x x(t)
 h(1)

 1;x x(t) 


.
..




 (σ1 )

 h1;x x(t) 


 h2;x x(t) 


..


(5.70)


.
 (σn −1 )

y

h
 ny −1;x x(t)

 h

 ny ;x x(t) 
 (1)

 hny ;x x(t) 


..




.
(σny )
hny ;x x(t)
has rank = nx , then this is a sufficient condition for observability. That this is a sufficient
condition can be realized since rank = nx of (5.70) implies full rank of (5.67). This
means that the first output is differentiated σ1 times, the second output is differentiated
σ2 times, and so on.
Often there are several ways to choose the set of integers σk , k = 1, . . . , ny . Typically
one wants to differentiate the outputs as few times as possible, so it is desirable to have as
low σk as possible. One way to choose the σk is therefore to first choose the largest σk as
small as possible, then make the next largest as small as possible and so on. If the σk are
chosen in this way, they are called the observability indices of the system (e.g., Nijmeijer
and van der Schaft, 1990).
The advantage of this method is that the number of differentiations of each output is
minimized. The method discussed e.g., in Theorem 5.3, differentiates all outputs the same
5.6
Other Insights Using Kunkel’s and Mehrmann’s Theory
101
number of times. However, if µ is chosen as small as possible in Theorem 5.3, then µ is
the smallest integer such that


hx x(t)
 h(1) x(t) 
 x



..


(5.71)
.


 (µ−2)

h x
x(t) 
(µ−1)
hx
x(t)
has full column rank and





hx x(t)
(1)
hx x(t)
..
.
(µ−2)
hx





(5.72)
x(t)
does not have full column rank. This means that µ − 1 is equal to the largest observability
index of the system (5.66). We formulate this as a proposition.
Proposition 5.1
Assume that the state-space system
ẋ(t) = f x(t)
y(t) = h x(t)
(5.73a)
(5.73b)
is locally weakly observable and fulfills the conditions of Theorem 5.3. Let µ in Theorem 5.3 be taken as small as possible. Then µ − 1 is equal to the largest observability
index of the system.
5.6.2
Zero Dynamics
In this section we will show how the ideas presented in this chapter also can be used
for examining zero dynamics. If a system is controlled so that it follows a prescribed
trajectory, then the zero dynamics is the dynamics that is not prescribed by this control
law. Assume for example that a DAE model
G ż(t), z(t), u(t), t = 0
(5.74a)
y(t) = h z(t)
(5.74b)
is to follow a prescribed trajectory y(t) = r(t), t ≥ 0 and that this trajectory can be
achieved by selecting an appropriate control signal u(t) and possibly initial condition
z(0). If all elements of z(t) are uniquely determined by this control law, then there are no
zero dynamics. But if some elements of z(t) can be given arbitrary initial conditions and
are not determined by the prescribed output, then these variables form the zero dynamics
of the system. The existence of zero dynamics can be examined by the methods that
previously were used to examine identifiability and observability. This can be done by
seeing (5.74) as a DAE where both z(t) and u(t) are unknown, but y(t) is known, y(t) =
r(t). If the system is observable, then there are no zero dynamics, since both z(t) and
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5
Identifiability and Observability for DAEs Based on Kunkel and Mehrmann
u(t) then are determined by (5.74). If the system is not observable, then there is either
zero dynamics or some components of u(t) are not necessary to control y(t), or both. If
observability is examined using Theorem 5.1, this leads to the following proposition.
Proposition 5.2
Assume that the extended DAE
G ż(t), z(t), u(t),
t =0
y(t) − h z(t)
{z
}
|
(5.75)
F
fulfills the conditions of Theorem 2.2 with
x(t) =
z(t)
u(t)
(5.76)
for some µ, a, d, and v and the solution x0 (t) and that all components of u(t) are uniquely
determined by any control law that achieves y(t) = r(t) for some function r(t). Then the
original DAE (5.74) has no zero dynamics if and only if a = nx where nx is the dimension
of x.
It can also be noted that if the extended DAE (5.75) is separated according to the
discussion in Section 2.2,
F̂1 (t, x1 , x2 , x3 , ẋ1 , ẋ2 , ẋ3 ) = 0
(5.77a)
F̂2 (t, x1 , x2 , x3 ) = 0
(5.77b)


x1 (t)
x(t) = Q x2 (t) , Q permutation matrix,
x3 (t)
(5.77c)
then F̂1 describes the zero dynamics of the system. This can be seen since these equations
can be locally solved to give
ẋ1 = L(t, x1 , x2 , ẋ2 )
(5.78a)
x3 = R(t, x1 , x2 )
(5.78b)
for some functions L and R. The zero dynamics are thus described by (5.78a).
Example 5.2: Zero dynamics and state-space models
To compare with results in the literature, consider the state-space model
ż(t) = f z(t) + g z(t) u(t)
y(t) = h z(t)
(5.79a)
(5.79b)
where z ∈ Rnz , u ∈ R, and y ∈ R. For simplicity we thus let the system be single-input
and single-output. Also let the z(t) in the system (5.79) be observable with u(t) and y(t)
known, so that


hz z(t)
(1)


hz z(t), u(t)


(5.80)


..


.
(j)
hz z(t), u(t), u̇(t), . . . , u(j−1)(t)
5.6
103
Other Insights Using Kunkel’s and Mehrmann’s Theory
(j)
has full (constant) rank for j = 0, . . . , nz . As before, hz is the partial derivative with
(j)
respect to z of the j:th time derivative of output. Similarly, let hu be the derivative
(µ)
with respect to u of the j:th time derivative of the output. Let hu be the first non-zero
derivative so that
h(j)
u = 0 j = 0, . . . µ − 1
(5.81a)
h(µ)
u 6= 0.
(5.81b)
We can now use Corollary 5.1 to examine observability of z(t) and u(t) in the model
(5.79) with y(t) known, and thereby also examine if there are any zero dynamics. Form
the (nz + 1)(µ + 1) × (nz + 1)(µ + 2) matrix

Fµ;x,ẋ,...,xµ+1
∗
hz
..
.





= ∗
 (µ−1)
hz

 ∗
(µ)
hz
∗
hu
..
.
I
0
0
0
···
···
..
.
0
0
..
.
0
0
..
.
0
0
..
.
∗
∗
0
∗
0
∗ ···
0 ···
∗ ···
0 ···
I
0
∗
0
0
0
∗
0
0
0
I
0
(µ−1)
hu
∗
(µ)
hu

0
0
.. 

.

0

0

0
0
where ∗ represent elements whose exact form are not important and
z
.
x=
u
(5.82)
(5.83)
(j)
Note that m = nz +1. By using that hu = 0, j = 0, . . . , µ−1 and by column operations
that do not change the rank of the matrix this can be written

0
hz
..
.





 0
 (µ−1)
hz

 0
0
0
0
..
.
I
0
0 ···
0 ···
..
.
0
0
..
.
0
0
..
.
0
0
..
.
0
0
0
0
0
0
0
0 ···
0 0
0 ···
0 0
I
0
0
0
0
0
0
0
0
0
I
0
(µ)
hu

0
0
.. 

.

0 .

0

0
0
(5.84)
By examining this matrix we get that
r = rank Fµ;x,ẋ,...,xµ+1 = (nz + 1)(µ + 1)
(5.85)
ν=0
(5.86)
and
since the matrix has full row rank. We also get
a = r − rank Fµ;ẋ,...,xµ+1 = r − nz (µ + 1) = µ + 1.
(5.87)
104
5
Identifiability and Observability for DAEs Based on Kunkel and Mehrmann
Taking

0 1 0 0 0
0 0 0 1 0

Z2T =  .
 ..
0
0
0
0
···
···
..
.
0

0
0



1
(5.88)
(j)
to pick out those rows in Fµ;x,ẋ,...,xµ+1 with hz , we get


hz
0
 h(1)
0 

 z

..
.. 
Z2T Fµ;x = 
. 

 .

 (µ−1)
h z
0 
(µ)
(µ)
hu
hz
(5.89)
so the (nz + 1) × (nz − µ) matrix T2 is taken so that the first nz rows are the orthogonal
complement to the µ first rows of (5.89), which gives that
d = rank Fẋ T2 = nz − µ
(5.90)
since rank Fẋ will pick out the first nz rows in T2 . We also have
m − a − v = nz + 1 − (µ + 1) − 0 = nz − µ
(5.91)
so Property 2.1 is satisfied. Similar calculations can also be performed with µ replaced
by µ + 1 to show that the conditions of Theorem 2.2 are fulfilled.
To conclude, we have that µ + 1 variables of z and u are determined by (5.79) when
y has a prescribed trajectory. Since u is used to control y, the variables that are not
determined by the equations must be taken from z. There are nz − µ such variables,
and these variables form the zero dynamics of the controlled system since they are not
determined by the control law. We see that there are no zero dynamics in the case when
µ = nz , which means that the output y must be differentiated exactly nz times for u to
appear. This is in line with standard results, see e.g., Isidori (1989).
5.7
Conclusions
In this chapter, new criteria for local identifiability and local weak observability of nonlinear differential-algebraic equations have been derived using results by Kunkel and
Mehrmann (2001). The inherent complexity of nonlinear differential-algebraic equations
make the criteria somewhat involved, but on the other hand the generality of DAE models
allows many models to fit into the framework. We have also shown that the criteria are
closely related to standard identifiability and observability criteria for state-space models,
and even extend these results in some cases.
We have also discussed how zero dynamics can be examined using the methods by
Kunkel and Mehrmann (2001).
6
Identifiability Tests Using Differential
Algebra for Component-Based
Models
This chapter discusses how the structure in component-based models can be used when
examining identifiability. We will show the interesting fact that once identifiability has
been examined for the components of a model, identifiability of the complete model can
be examined using a reduced number of equations.
6.1
Introduction
In Section 3.4, a general method by Ljung and Glad (1994) for examining identifiability in linear and nonlinear systems, both state-space systems and differential-algebraic
equations, was summarized. This method uses differential algebra which suffers from
high computational complexity, and can therefore only handle quite small systems. This
chapter discusses how the modularized structure in component-based models can be used
to speed up the computations. Since modeling tools such as Modelica are based on
component-based modeling, the approach can be useful for models created using such
tools.
As discussed in Section 2.1.1, a component-based model consists of a number of
components, with equations describing them, and a number of equations describing the
connections between the components. Since the components represent different physical
parts of the system, it is natural that they have independent parameters so that will be
assumed in the present chapter. As before (Section 2.1.1), the equations describing a
model with m components are written as
fi li (t), wi (t), θi , p = 0
i = 1, . . . , m.
(6.1)
Here, li (t) ∈ Rnli are internal variables, wi (t) ∈ Rnwi external variables that are used in
the connections and θi ∈ Rθi unknown parameters, all in component i. As before p is the
105
106
6
Identifiability Tests Using Differential Algebra for Component-Based Models
differentiation operator with respect to time,
dx(t)
.
(6.2)
dt
With fi (·) ∈ Rnfi , it is assumed that nfi ≥ nli so that there are at least as many equations
as internal variables for each component. The equations describing the connections are
written


w1 (t)


(6.3)
g u(t), w(t) = 0, w(t) =  ... 
px(t) =
wm (t)
where u(t) is an external input signal. Measured output signals are specified as
y(t) = h w(t)
(6.4)
where we have assumed that no unknown parameters are included in the measurement
equation. Parameters in the measurement equation could instead be handled by introducing extra components that for example scale the measured output. To summarize, a
complete component-based model consists of the equations for the components, for the
connections, and for the measurements,
fi li (t), wi (t), θi , p = 0
i = 1, . . . , m.
(6.5a)
g u(t), w(t) = 0
(6.5b)
y(t) = h w(t) .
(6.5c)
Identifiability of this model can be analyzed using the method described in Section 3.4. However, our main idea is to separate the identifiability analysis into two stages.
The first stage is to rewrite the model for a single component using the technique given
by (3.21), and thus avoiding this computation for the complete model. The second stage
is to examine identifiability by combining the transformed equations for each component.
For the first stage, we thus assume that the model
fi li (t), wi (t), θi , p = 0
(6.6)
can be rewritten in the equivalent form
Ai,1 (wi , p) = 0
..
.
Ai,nwi (wi , p) = 0
Bi,1 (wi , θi,1 , p) = 0
Bi,2 (w, θi,1 , θi,2 , p) = 0
..
.
Bi,nθi (wi , θi,1 , θi,2 , . . . , θi,nθi , p) = 0
Ci,1 (wi , θi , li , p) = 0
..
.
Ci,nli (wi , θi , li , p) = 0.
(6.7)
6.1
107
Introduction
Note that if the original DAE only has polynomial equations, this transformation is always
possible. The Ai equations are relations that the external variables must satisfy, regardless
of the value of the parameters θ. The Bi equations can be used to determine identifiability
of the parameters if the wi are known, and forms a linear regression for the parameters if
the component is globally identifiable and the equations are polynomial. The Ci equations
give relations for the internal variable li and are of no further interest in this chapter.
An important part of the model for the analysis below is the set of Ai,j . These relations
between the connecting variables are independent of the choice of the parameters.
In the examples below, we discuss how the form (6.7) can be calculated for a model
of a capacitor, a model of an inductor and a model of a nonlinear resistor.
Example 6.1: Capacitor component
Consider a capacitor described by the voltage drop w1 , current w2 and capacitance θ1 . It
is then described by (6.6) with
θ1 ẇ1 − w2
.
(6.8)
f1 =
θ̇1
If we consider only situations where ẇ1 6= 0 we get the following series of equivalences.
θ1 ẇ1 − w2 = 0,
θ1 ẇ1 − w2 = 0,
θ1 ẇ1 − w2 = 0,
θ1 ẇ1 − w2 = 0,
θ̇1 = 0,
ẇ1 6= 0
⇔
θ1 ẅ1 − ẇ2 = 0,
⇔
θ1 ẇ1 ẅ1 − ẇ1 ẇ2 = 0,
⇔
w2 ẅ1 − ẇ1 ẇ2 = 0,
ẇ1 6= 0
ẇ1 6= 0
ẇ1 6= 0
With the notation (6.7) we thus have
A1,1 = w2 ẅ1 − ẇ1 ẇ2
(6.9a)
B1,1 = θ1 ẇ1 − w2
(6.9b)
and the function s1 of (3.22) is ẇ1 .
Example 6.2: Inductor component
Next consider an inductor where w1 the voltage, w2 is the current, and θ1 the inductance.
It is described by
θ1 ẇ2 − w1
f2 =
.
(6.10)
θ̇1
Calculations similar to those of the previous example show that this is equivalent to
provided ẇ2 6= 0.
A2,1 = ẅ2 w1 − ẇ2 ẇ1
(6.11a)
B2,1 = θ1 ẇ2 − w1
(6.11b)
108
6
Identifiability Tests Using Differential Algebra for Component-Based Models
As discussed earlier, the transformation to (6.7) can always be performed for polynomial DAEs. To show that calculations of this type in some cases also can be done for
non-polynomial models, we consider a nonlinear resistor where the voltage drop is given
by an arbitrary function.
Example 6.3: Nonlinear resistor component
Consider a nonlinear resistor with the equation
w1 = R(w2 , θ1 )
(6.12)
where it is assumed that the parameter θ1 can be uniquely solved from (6.12) if the voltage
w1 and the current w2 are known, so that
θ1 = φ(w1 , w2 )
(6.13)
for some function φ. Differentiating (6.12) once with respect to time and inserting (6.13)
gives
(6.14)
ẇ1 = Rw2 w2 , φ(w1 , w2 ) ẇ2
which is a relation between the external variables w1 and w2 . We use the notation Rx for
the partial derivative of R with respect to the variables x. We thus get
A3,1 = Rw2 w2 , φ(w1 , w2 ) ẇ2 − ẇ1
(6.15a)
B3,1 = θ1 − φ(w1 , w2 ).
(6.15b)
In the special case with a linear resistor, where R = θ1 · w2 , A3,1 reduces to
w1
ẇ2
w2
⇔ w2 ẇ1 = w1 ẇ2
ẇ1 =
(6.16a)
(6.16b)
(assuming w2 6= 0).
6.2
Main Results
The main results of this chapter concern how the modularized structure of componentbased models can be used to examine identifiability in an efficient way.
Assume that all components are identifiable if the external variables wi of each component are measured. This means, that given measurements of
wi
i = 1, . . . , m
(6.17)
the unknown parameters θ can be computed uniquely from the B polynomials. When
examining identifiability of the connected system it is not a big restriction to assume that
the individual components are identifiable since information is removed when not all wi
are measured. (Recall that all components have unique parameters.)
6.2
109
Main Results
When the components have been connected, the only
knowledge available about
the
wi is the A polynomials and the equations g u(t), w(t) = 0 and y(t) = h w(t) . The
connected system is thus identifiable if the wi can be computed from
Aij wi (t), p = 0
(
i = 1, . . . , m
j = 1, . . . , nAi
g u(t), w(t) = 0
y(t) = h w(t)
(6.18a)
(6.18b)
(6.18c)
when u(t) and y(t) are known. Note that this means that all w(t) are algebraic variables
(not differential), so that no initial conditions can be specified for any component of w(t).
If, on the other hand, there are several solutions to the equations (6.18) then these different solutions can be inserted into the B polynomials, so there are also several possible
parameter values. In this case the connected system is therefore not identifiable.
The result is formalized in the following theorems. Note that the distinction between
global and local identifiability was not discussed above, but this will be done below.
6.2.1
Global Identifiability
Global identifiability means that there is a unique solution to the identification problem,
given that the measurements are informative enough. For a component (6.6) that can be
rewritten in the form (6.7) global identifiability means that the Bi,j can be solved uniquely
to give the θi,j . In other words there exist functions ψ that can in principle be calculated
from the Bi,j , such that
θi = ψi (wi , p).
(6.19)
When the DAE consists of polynomial equations, the ψ are formed from linear regressions,
Pi (wi , p)θi − Qi (wi , p) = 0.
(6.20)
We have the following formal result on identifiability.
Theorem 6.1
Consider a component-based model where the components (6.6) are globally identifiable
with wi measured and thus can be described in the form (6.19). A sufficient condition for
the total model to be globally identifiable is that (6.18) is observable with respect to the
wi . If all the functions ψi of (6.19) are injective then this condition is also necessary.
Proof: If (6.18) gives a global solution for w(t), then this solution can be inserted into
the B polynomials to give a global solution for θ since the components are globally identifiable. The connected system is thus globally identifiable. If there are several solutions
for wi and the functions ψi of (6.19) are injective, then there are also several solutions for
θ, so the system is not globally identifiable since the identification problem has more than
one solution.
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6.2.2
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Identifiability Tests Using Differential Algebra for Component-Based Models
Local Identifiability
Local identifiability of a model structure means that locally there is a unique solutions
to the identification problem, but globally there may be more than one solution. This
means that the description (6.19) is valid only locally. We get the following result on
local identifiability.
Theorem 6.2
Consider a component-based model where the components (6.6) are locally identifiable
with wi measured and thus can be locally described in the form (6.19). A sufficient condition for the total model to be locally identifiable is that (6.18) is observable with respect
to the wi . If all the functions ψi of (6.19) are locally injective then this condition is also
necessary.
Proof: If (6.18) gives a locally unique solution for w(t), then this solution can be inserted into the B polynomials to give a local solution for θ since the components are
locally identifiable. The connected system is thus locally identifiable. If there locally are
several solutions for wi and the functions ψi of (6.19) are injective, then there are also several local solutions for θ, so the system is not locally identifiable since the identification
problem locally has more than one solution.
6.3
Applying the Results
The techniques discussed above are intended to be used when examining identifiability for
component-based models. Since each component must be transformed into the form (6.7),
the first step is to perform these transformations using, e.g., differential algebra (Ljung
and Glad, 1994). The transformed version of the components can then be stored together
with the original model equations in model libraries. As the transformation is calculated
once and for all, it should also be possible to use other methods than differential algebra
to make the transformation into the form (6.7). As mentioned above, this could make it
possible to handle systems described by non-polynomial differential-algebraic equations.
When a component-based model has been composed of components for which the
transformation into the form (6.7) is known, identifiability of the complete model,
fi li (t), wi (t), θi , p = 0
i = 1, . . . , m.
(6.21a)
g u(t), w(t) = 0
(6.21b)
y(t) = h w(t)
(6.21c)
can be checked by examining the solutions of the differential-algebraic equation (6.18),
(
i = 1, . . . , m
(6.22a)
Aij wi (t), p = 0
j = 1, . . . , nwi
g u(t), w(t) = 0
(6.22b)
y(t) = h w(t) .
(6.22c)
The number of solutions to this differential-algebraic equation then determines if the system is identifiable, as discussed in Theorem 6.1 and 6.2. Note that the number of solutions
6.4
111
Examples
could vary with t, so that the system is identifiable at only some time instances. The number of solutions of the differential-algebraic equation (6.22) could be checked in different
ways, and some are listed below.
Differential Algebra
If the system equations are polynomial, then one way to check the number of solutions
is to use differential algebra in a similar way as was done to achieve the form (6.7). This
method can be slow in some cases, but it always gives definite answers. However, in some
cases this approach should be faster than to derive the transformation to the form (6.7) for
the complete component-based model. Differential algebra can be used to examine both
local and global identifiability, but requires that the equations are polynomial.
Kunkel & Mehrmann’s Test
The analysis method by Kunkel and Mehrmann (2001) that is discussed in Section 2.2
examines the properties of nonlinear differential-algebraic equations through certain rank
tests. Among other things, it is possible to use these results for examining local observability, as discussed in Section 5.3. One possibility to examine observability of (6.22) is
thus to use the results in Section 5.3.
Manual Inspection
For smaller models it may be possible to examine the solvability of (6.22) by inspection
of the equations and manual calculations. This can of course not be developed into a
general procedure, but may still be a good approach in some cases. Manual inspection
can be used to check both local and global identifiability.
6.4
Examples
In this section the techniques described in the chapter are exemplified on a very small
model library consisting of a resistor model, an inductor model, and a capacitor model.
Note that these components have corresponding components for example within mechanics and fluid systems. (Compare bond graphs, where generic components are used to
model phenomena from all these fields.) In this small example, all variables are external.
The transformation into the form (6.7) was performed in Examples 6.1, 6.2, and 6.3,
so we shall here examine the identifiability of different connections of the components.
In the first example we consider the connection of a resistor and an inductor in series.
Example 6.4: Resistor and inductor in series
u
w1
+ w2
w3
+
Figure 6.1: A resistor and an inductor connected in series.
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Identifiability Tests Using Differential Algebra for Component-Based Models
Consider a nonlinear resistor and an inductor connected in series where the current w2 =
f and total voltage u are measured as shown in Figure 6.1. Denote the voltage over the
resistor with w1 and the voltage over the inductor with w3 . Using Examples 6.2 and 6.3
we get the equations
ẇ1 = Rw2 w2 , φ(w1 , w2 ) ẇ2
(6.23a)
ẅ2 w3 = ẇ2 ẇ3
(6.23b)
for the components. The connection is described by the equation
w1 + w3 = u.
(6.23c)
Differentiating the last equation once gives
ẇ1 + ẇ3 = u̇.
(6.23d)
The system of equations (6.23) (with w1 , ẇ1 , w3 , and ẇ3 as unknowns) has the Jacobian


−Rw2 ,w1 ẇ2 1 0
0

0
0 ẅ2 −ẇ2 


(6.24)

1
0 1
0 
0
1 0
1
where
Rw2 ,w1 =
∂
Rw2 w2 , φ(w1 , w2 ) .
∂w1
(6.25)
The Jacobian has the determinant −Rw2 ,w1 · ẇ22 + ẅ2 , so the system of equations is
solvable for most values of the external variables. This means that the system is locally
identifiable.
In the next example, two capacitors are connected in series.
Example 6.5: Two capacitors in series
u
w1
w3
+ w2
+
Figure 6.2: Two capacitors connected in series.
Now consider two capacitors connected in series where the current w2 = f and total
voltage u are measured as shown in Figure 6.2. Denote the voltages over the capacitors
with w1 and w3 respectively. Using Example 6.1 we get the equations
w2 ẅ1 = ẇ1 ẇ2
(6.26a)
w2 ẅ3 = ẇ3 ẇ2
(6.26b)
6.5
113
A Mechanics Model Library
for the components and the equation
w1 + w3 = u
(6.27)
for the connections. These equations directly give that if
w1 (t) = φ1 (t)
(6.28a)
w3 (t) = φ3 (t)
(6.28b)
is a solution, then so are all functions of the form
w1 (t) = (1 + λ)φ1 (t)
(6.29a)
w3 (t) = φ3 (t) − λφ1 (t)
(6.29b)
for scalar λ. Since (6.9b) implies that the capacitance is an injective function of the
derivative of the voltage, this shows that the system is not identifiable.
6.5
A Mechanics Model Library
In this section we will further exemplify the methods discussed in the chapter by making
the transformation into the form (6.7) for all continuous components in the Modelica
model library Modelica.Mechanics.Translational (Fritzson, 2004). This library contains
the following components for one-dimensional movement, such as masses and springs.
SlidingMass models a mass moving in one dimension.
Stop models a mass that hits a stop such as a wall.
Rod models a massless rod.
Spring models a one-dimensional spring.
Damper models damping.
SpringDamper models a spring and a damper connected in parallel.
ElastoGap models a spring damper in combination with a gap.
Position models a control input to position.
Accelerate models a control input to acceleration.
Fixed models a point that is fixed.
Force models a controlled force input.
RelativeStates is used for different coordinate systems in different parts of a model.
Examples contain a set of example models that use the components.
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Identifiability Tests Using Differential Algebra for Component-Based Models
Interfaces are base models that are used when defining the components.
Sensors are used to model measurements.
The components Position, Accelerate, Fixed, Force, and Sensors are used to model inputs
and outputs, and are therefore included in the connection equations (6.3). RelativeStates
is also assumed to be included among the connections. Therefore the equations describing these equations do not need to be transformed. The components Stop and ElastoGap contain discontinuous dynamics and cannot be handled by the theory presented here.
The components we will consider are therefore SlidingMass, Rod, Spring, Damper, and
SpringDamper. First consider the SlidingMass component.
Example 6.6: SlidingMass
The SlidingMass is a component that describes a mass that slides along a surface
without friction. It is described by the equation
ms̈(t) = f1 (t) + f2 (t)
(6.30)
where the position s and forces f1 and f2 are external variables and the mass m is a
parameter. There are no internal variables. Applying Ritt’s algorithm to this equation
gives
A = (f1 + f2 )s(3) − (f˙1 + f˙2 )s(2)
(6.31a)
B = ms̈ − (f1 + f2 )
(6.31b)
which is in the desired form (6.7). The component is globally identifiable.
Next, consider the Rod component.
Example 6.7: Rod
The Rod component describes a rod without mass. It translates the force one end to
its other end. It is described by the equation
f1 (t) + f2 (t) = 0
(6.32)
where the forces f1 and f2 are external variables. There are no internal variables or
parameters, so this is already in the form (6.7) with
A = f1 + f2 .
Now consider the Spring component.
(6.33)
6.5
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A Mechanics Model Library
Example 6.8: Spring
The spring component models an ideal linear spring. The equation describing it is
f (t) = c s1 (t) − s2 (t)
(6.34)
where the force f and positions of each end of the spring s1 and s2 are external variables.
The spring constant c is a parameter. There are no internal variables. Applying Ritt’s
algorithm gives
A = (ṡ1 − ṡ2 )f − (s1 − s2 )f˙
(6.35a)
B = f − c(s1 − s2 )
(6.35b)
which is in the form (6.7). The component is globally identifiable.
Next consider the Damper.
Example 6.9: Damper
The Damper component models a linear damper. It is described by the equation
f (t) = d ṡ1 (t) − ṡ2 (t) .
(6.36)
The force f and position s are external variables and the spring constant d is a parameter.
There are no internal variables. Ritt’s algorithm gives the form (6.7),
A = (s̈1 − s̈2 )f − (ṡ1 − ṡ2 )f˙
(6.37a)
B = f − d(ṡ1 − ṡ2 ).
(6.37b)
The component is globally identifiable.
Finally, we consider the SpringDamper component.
Example 6.10: SpringDamper
This component which represents a spring and a damper connected in parallel, is
described by the equation
f (t) = c s1 (t) − s2 (t) + d ṡ1 (t) − ṡ2 (t) .
(6.38)
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Identifiability Tests Using Differential Algebra for Component-Based Models
Ritt’s algorithm gives
(3)
(3)
A = f¨(ṡ1 − ṡ2 )2 − (ṡ1 − ṡ2 )f˙(s̈1 − s̈2 ) − (ṡ1 − ṡ2 )f (s1 − s2 ) +
(3)
(3)
f˙(s1 − s2 )(s1 − s2 ) + f (s̈1 − s̈2 )2 − (s̈1 − s̈2 )(s1 − s2 )f¨
B1 = c (ṡ1 − ṡ2 )2 − (s̈1 − s̈2 )(s1 − s2 ) − (ṡ1 − ṡ2 )f˙ + f (s̈1 − s̈2 )
B2 = d − (ṡ1 − ṡ2 )2 + (s̈1 − s̈2 )(s1 − s2 ) + f (ṡ1 − ṡ2 ) − (s1 − s2 )f˙.
(6.39a)
(6.39b)
(6.39c)
The component is globally identifiable.
We will now consider a connection of the components and examine if it is identifiable.
Example 6.11: Connected components
Consider a SpringDamper component connected between a fixed point and a SlidingMass
component. Figure 6.3 shows how graphical modeling of this system in Modelica would
look.
SpringDamper
SlidingMass
Fixed
Figure 6.3: A SpringDamper and SlidingMass connected in Modelica.
The A polynomials of the components are
(3)
(3)
A1 = f¨(ṡ1 − ṡ2 )2 − (ṡ1 − ṡ2 )f˙(s̈1 − s̈2 ) − (ṡ1 − ṡ2 )f (s1 − s2 ) +
(3)
(3)
f˙(s1 − s2 )(s1 − s2 ) + f (s̈1 − s̈2 )2 − (s̈1 − s̈2 )(s1 − s2 )f¨
A2 = (f1 + f2 )s(3) − (f˙1 + f˙2 )s(2) .
(6.40a)
(6.40b)
The components are connected so that the position s of the SlidingMass is equal to the
second position s2 of the SpringDamper and the force of the SpringDamper f is equal to
the force f1 of the SlidingMass. Furthermore, the SpringDamper is connected to a fixed
point so that s1 = 0, the force f2 of the SlidingMass is controlled by the signal u(t) and
the position s(t) is measured to give y(t). This gives the connections


s1
 s2 − s 


(6.41)
f − f1  = 0
f2 − u
| {z }
g
6.6
117
Conclusions
and the measurement
y = s.
(6.42)
Since this means that all signals but f (or equivalently f1 ) are known, we need to examine if this signal is solvable from (6.40). Adding the derivative of (6.40b) to the equations (6.40) gives the following system of equations for f and its derivatives (where we
have used f = f1 , s1 = 0, and s2 = s).
−ṡs(3) + s̈2

s(3)
s(4)

 
f
−ṡs̈ + s(3) s ṡ2 − s̈s
(2)


f˙ =
−s
0
(2)
0
−s
f¨


0


−f2 s(3) + f˙2 s(2)
(3)
−f˙2 s + f¨2 s(2) − f2 s(4) + f˙2 s(3)
(6.43)
The 3 × 3 matrix is invertible for most values of s and its derivatives, so the system is
globally identifiable.
Note that we choose to differentiate (6.40b) since that did not introduce any new
unknown variables.
6.6
Conclusions
The main conclusion that can be drawn from the discussions in this chapter is that identifiability for a component-based model can be examined using parameter-free equations.
If all components are identifiable and have independent parameters, identifiability is completely determined by the parameter-free equations A, the connector equations g and the
measurement equation y = h.
While this result is of interest in itself, an application of it to simplify examination of
identifiability in component-based models. For components in model libraries, the transformation to the form (6.7) is computed once and for all and stored with the component.
This makes it possible to only consider a smaller number of equations when examining
identifiability for a component-based model composed of such components. Although
the method described in this chapter may suffer from high computational complexity (depending, among other things, on the method selected for deciding the number of solutions
for (6.22)), it can make the situation much better than when trying to use the differentialalgebra approach described by Ljung and Glad (1994) directly on a complete model.
Future work could include to examine if it is possible to make the method fully automatic, so that it can be included in modeling tools and to examine if other system analysis
or design methods can benefit from the modularized structure in component-based models. It could also be interesting to examine the case when several components share the
same parameter. This could occur for example if the different parts of the system are
affected by environmental parameters such as temperature and fluid constants.
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6
Identifiability Tests Using Differential Algebra for Component-Based Models
7
Simulation-Based Tests for
Identifiability
In this chapter we discuss how DAE solvers can be used for examining identifiability.
The basic idea is the same as in the two previous chapters — extend the DAE with the
equation θ̇(t) = 0 and examine if the extended DAE is observable. In this chapter, this is
examined using DAE solvers.
7.1
Introduction
The development of object-oriented modeling languages such as Modelica has led to the
development of effective solvers for large nonlinear DAE systems. These solvers were
discussed in Section 2.5. The solvers are of course mainly intended for simulation of
models, but in this chapter we will discuss how they also can be used to examine identifiability of DAE models. The method discussed here is inspired by the differential algebra
approach discussed in Section 3.4.
The basic principles behind the method presented here are shown in the following
example.
Example 7.1: Introductory example
Consider again the model structure (3.25a)
ÿ(t) + 2θ0 ẏ(t) + θ02 y(t) = 0
(7.1)
which was examined by Ljung and Glad (1994). This model structure is globally identifiable, which was proved by Ljung and Glad (1994). Here it will be shown how a DAE
solver can be used to prove local identifiability. If (7.1) is identifiable, it should be possible to compute the value of θ0 given measurements of y generated from (7.1), in other
119
120
7
Simulation-Based Tests for Identifiability
words the system of equations
ÿ(t) + 2θ0 ẏ(t) + θ02 y(t) = 0
2
ÿ(t) + 2θ(t)ẏ(t) + θ (t)y(t) = 0
(7.2a)
(7.2b)
should be uniquely solvable for θ(t). This means that given a value of θ0 and initial
conditions for y(t) and its derivatives, a DAE solver should be able to compute θ(t), and
we should have θ(t) ≡ θ0 . The solver also computes y(t) from (7.2a), but this variable is
not of interest when examining identifiability.
Simulating (7.2) using Dymola for θ0 = 3, y(0) = 0, and ẏ(0) = 1 gives the solution
θ(t) ≡ 3, so the model structure is locally identifiable at 3. Simulations with other values
of θ0 give corresponding results.
The idea behind the method is thus to examine if the identification problem has several
solutions by solving certain equations with a DAE solver. This chapter discusses which
equations that should be solved to examine identifiability and how the results should be
interpreted.
7.2
Basic Setup
In this chapter, identifiability will be discussed for general nonlinear DAE systems,
F ẋ(t), x(t), θ, u(t) = 0
(7.3a)
y(t) = h x(t), θ .
(7.3b)
However, the exact number of equations in the system is essential for the discussion.
Therefore we will consider (7.3) as a set of scalar equations,
gi (u, y, x, ẋ, θ) = 0
i = 1, 2, . . . , r.
(7.4)
The dimensions of the variables are
dim u = nu
(7.5a)
dim y = ny
dim x = nx
(7.5b)
(7.5c)
dim θ = nθ .
(7.5d)
nẋ = the number of x that appear differentiated.
(7.5e)
r = ny + nx
(7.6)
Also,
It is assumed that
so that there are the same number of equations (gi ) as unknowns (x and y) if u and θ are
given. Furthermore it is assumed that the functions gi can be differentiated with respect
to time as many times as necessary.
7.3
121
Examining Identifiability
In the system identification problem, it is known that the parameters are constant, i.e.,
θ̇ = 0.
(7.7)
The equation system (7.4) and (7.7) is generally not solvable for arbitrary signals u and
y since the number of equations, r + nθ = ny + nx + nθ , is larger than the number of
unknowns, nx + nθ . This means that they cannot be plugged directly into a DAE solver
to examine identifiability. However, if the signals u and y come from an identical system,
with some fixed parameter values θ0 and internal variables x(t), the system (7.4) and (7.7)
must have at least one solution, since one solution is θ ≡ θ0 and x(t). If the system is
not identifiable, there will be more solutions than this one. The complete problem when
examining identifiability at θ0 is thus to check if the following problem has a unique
solution θ = θ0 :
gi (u, y, x, ẋ, θ0 ) = 0
˙ θ) = 0
gi (u, y, x̃, x̃,
θ̇ = 0
i = 1, 2, . . . , r
(7.8a)
i = 1, 2, . . . , r
(7.8b)
(7.8c)
Here, u and θ0 shall be given and the solver is to compute θ, x, x̃, and y. We still do
not have the same number of equations (2r + nθ = 2ny + 2nx + nθ ) as unknowns
(ny + 2nx + nθ ). This will be further discussed in the following section. Note that it is
central that (7.8a) and (7.8b) have the same inputs and outputs u and y.
If there is only one solution, it must be θ ≡ θ0 and the system is globally identifiable at θ0 . If there are a number of distinct constant solutions θ, the system is locally,
but not globally identifiable at θ0 . If there are an infinite number of solutions in every
neighborhood of θ0 , then the system is neither globally nor locally identifiable at θ0 .
The identifiability properties of a model structure is normally influenced by if the
initial condition x(0) is known or has to be estimated. The most common case in applications is perhaps that x(0) is unknown, but it could be known for example if an experiment
is started in an equilibrium.
7.3
Examining Identifiability
The basic idea of the method proposed here is to solve the system of differential-algebraic
equations (7.8) with respect to θ, x, x̃, and y given u and θ0 using a DAE solver. If there is
a locally unique solution θ ≡ θ0 , the system is locally identifiable. However, the equation
system has more equations than unknowns, so it cannot be directly plugged into a DAE
solver. (As was discussed in Section 2.5, currently available DAE solvers require that the
number of equations is the same as the number on unknowns.) To resolve this issue, a
preprocessing step is added where the equations are manipulated so that the number of
equations and unknown become the same.
This section describes how the equations (7.8) should be preprocessed to allow them
to be solved using a DAE solver, and how conclusions about identifiability can be drawn
from the solution.
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7.3.1
7
Simulation-Based Tests for Identifiability
Preprocessing
Basically there are three problems with the description (7.8) when it comes to simulation
with a DAE solver:
1. The number of equations 2r +nθ = 2ny +2nx +nθ are not the same as the number
of unknowns ny + 2nx + nθ . As was discussed in Section 2.5, currently available
DAE solvers cannot handle this situation.
2. Some of the θ may be selected as states by the solver so that initial conditions must
be specified. This is a problem since the goal is to compute the value of θ.
3. Some of the x̃ may be selected as states so that initial conditions must be specified.
This is acceptable if x(0) is known in the identification problem under examination,
but otherwise this is undesirable.
Problem 2 is caused by the fact that derivatives of θ are included in the system of
equations. To resolve problem 1 and 2, (7.8c) must be removed. At the same time new
equations must be added by differentiating the given equations to make the number of
unknowns the same as the number of equations. Note that the unknowns are y, x, x̃, and
θ so the number of unknowns is initially ny + 2nx + nθ , and that the number of equations
(excluding θ̇ = 0 which should be removed) is 2r = 2ny + 2nx . If ny < nθ (which
is usually the case), nθ − ny equations plus one equation for each new variable that is
introduced in the process must be added.
The transformations may introduce new solutions that were not present in (7.8). However, the only equations that have been removed are (7.8c), so if the solution satisfies
θ̇ = 0 it must be a solution of (7.8). This will be utilized later.
The case with unknown initial conditions x(0) is more involved, so the cases with
known and unknown initial conditions are discussed separately below.
Known Initial Conditions
If the initial conditions are known, what needs to be done is to create nθ − ny new equations by differentiating equations from (7.8b) with respect to time. The equations that
are differentiated should be chosen among those containing θ since the knowledge that
θ̇ = 0 can be utilized here. Differentiating other equations actually does not introduce
any new information to the DAE solver, since it can differentiate equations algebraically.
It is preferable to differentiate several different equations containing elements of θ since
higher derivatives may make the equations more difficult to handle for the DAE solver.
Unknown Initial Conditions
If the initial conditions x(0) are unknown, it is not acceptable if x̃ are selected as states
by the DAE solver. To reduce the risk of this happening, x̃˙ are not marked as derivatives
of x̃ for the solver, but merely as time-dependent variables. To emphasize this, we will
˙ This will introduce nẋ new variables, so more equations need to
write x̃p instead of x̃.
be differentiated than for known initial conditions. Also, when differentiating, e.g., an
equation containing x̃p this will produce the second derivative of x̃, which will also be a
7.3
Examining Identifiability
123
new variable, denoted x̃pp . This means that more equations need to be differentiated in
this case than for the case with known initial conditions.
When differentiating equations, one should select equations to be differentiated in
systematic manner, so that not too many equations are differentiated. First, equations that
do not introduce new variables (e.g., x̃pp ) should be differentiated. After that, groups
of equations that when differentiated give more new equations than unknowns should be
differentiated. In this process, equations containing θ should be selected first since the
knowledge that θ̇ = 0 can be utilized here. Also note that all derivatives of y should be
considered as known since the DAE solver can compute them from (7.8a). This process
will eventually give the same number of equations as unknowns.
The procedure outlined above can be formalized using minimally structurally singular (MSS) sets. MSS sets were introduced by Pantelides (1988), where they were used to
examine which equations to differentiate to find conditions that consistent initial conditions of a DAE must satisfy. Here they will also be used to find equations to differentiate,
but with a slightly different objective. A set of equations is structurally singular with
respect to a set of variables if the number of equations are greater than the number of
variables, and a set of equations is minimally structurally singular if it is structurally singular, and none of its proper subsets are structurally singular. MSS sets are useful since
when differentiating such a set of equations, it will produce more new equations than new
differentiated variables. The following property of MSS sets will be needed.
Lemma 7.1
If a set of equations is MSS with respect to the variables occurring in the equations, then
the number of equations is exactly one more than the number of variables.
Proof: The number of equations must be greater than the number of variables, otherwise
the set of equations would not be structurally singular. If the number of equations exceeds
the number of variables by two or more, it would be possible to remove one equation
and still have a set of equations that is structurally singular. Therefore, the number of
equations must exceed the number of variables by exactly one.
The following algorithm for differentiating equations can now be formulated:
1. Let E be the original set of nx + ny equations in (7.8b).
2. Let z be a set with nx elements, that for each element in x̃ contains the highest
derivative that occurs in E.
3. Find a set of equations from E that is MSS with respect to the variables in z occurring in the set. This can be done, e.g., using Algorithm 3.2 in Pantelides (1988).
Preferably, the MSS set should have as few equations as possible.
4. Differentiate the equations in the MSS set. According to Lemma 7.1, the number
of new equations generated will exceed the number of new variables generated by
one.
5. In E, replace the equations in the MSS set with their differentiated versions.
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Simulation-Based Tests for Identifiability
6. Repeat from 2 until the number of equations, including those in E and those that
have been removed from E, equals the number of unknowns, that is the number of
x including differentiated versions plus the number of θ.
The algorithm will terminate since the difference between the number of equations and
number of unknowns is reduced by one each time it reaches Step 4.
7.3.2
Drawing Conclusions on Identifiability
After the preprocessing step, there are as many equations as unknowns, so the transformed
equations can be plugged into a DAE solver. What should be done now is thus to simulate
the transformed equations, and examine if there is a unique solution with θ ≡ θ0 . Before
simulating, the input signal u, initial condition x(0) and value of θ0 should be selected
to the values where identifiability should be checked. Here it can be noted that identifiability properties often are the same for most inputs, initial states and θ0 , see e.g., Ljung
and Glad (1994). Furthermore, the most time consuming parts of the process are the preprocessing and index reduction step in the solver, and these do not have to be repeated
when changing u, x(0), and θ0 . This means that several different choices can be tested
with small computational effort. After making these choices and running the DAE solver,
there are basically five situations that may occur which lead to different conclusions on
the identifiability. These situations are discussed below.
The Solution θ is Constant, and θ ≡ θ0 :
The only equation that was removed from the original set of equations is (7.8c). This
equation is still fulfilled since θ is constant, so the present solution is a solution of the
original equations (7.8). Furthermore, since the DAE solver is assumed to give an error
message if a solution is not locally unique, the solution θ ≡ θ0 is locally unique. This
gives that the system is locally identifiable at θ0 .
The Solution θ is Constant, and θ 6≡ θ0 :
As in the case where θ ≡ θ0 , it is clear that the present solution is a solution of the
original equations (7.8) as θ is constant. Since θ 6≡ θ0 it is proved that there are two
different values of θ that give the same input-output behavior, so the model is not globally
identifiable at θ0 . However, it is locally identifiable at the constant value θ since this
solution is locally unique according to the assumptions on the DAE solver.
If it is desirable to determine if the model is locally identifiable also at θ0 , one should
go back and run the simulation with a different choice of u and/or x(0) to see if the new
solution has θ ≡ θ0 .
If the functions gi are polynomial, the results by Ljung and Glad (1994) give that a
model structure is either locally identifiable for almost all θ or for no θ. If the gi are
polynomial, it is therefore clear that the model structure is locally identifiable at almost
all θ.
7.3
Examining Identifiability
125
The Solution θ is Time-Varying:
The original set of equations included θ̇ = 0, so any time-varying solutions must have
been introduced by the preprocessing step. This a situation that does not give information
identifiability. To achieve a solution with constant θ, it may in some cases be sufficient to
change u and x(0). If this does not produce a constant θ, it is necessary to return to the
preprocessing step and differentiate a different set of equations.
The DAE Solver Indicates that Existing Solutions are not Locally Unique:
If existing solutions are not locally unique, the preprocessing step has either introduced
new solutions, or the model structure is not locally identifiable at θ0 . The case with
solutions that have been added in the preprocessing step can be handled by returning to the
preprocessing, differentiating a different set of equations and then running the simulation
again. If the cause of a non-unique solution is that the model structure is not locally
identifiable at θ0 , then this can be verified by computing which parameters or functions
of the parameters that are identifiable. This is discussed in Section 7.3.3.
The DAE Solver Indicates that no Solutions Exist:
This is a degenerate case since it is clear that at least one solution exists: x̃ = x and
θ = θ0 . The reason is usually that the initial condition x(0) gives problems for the
solver. For example, x(0) = 0 may give rise to problems. If this problem occurs, run the
simulation again with a new selection of x(0) and/or u.
The discussion above can be summarized with the following result.
Result 7.1
Assume that if a locally unique solution to (7.8) exists, then it is given by the DAE solver.
Otherwise the user is notified that no solution exists or that existing solutions are not
locally unique. Then, for the five situations that can occur when simulating the preprocessed identifiability problem, the following conclusions can be made about identifiability
for the selected input u and initial state x(0):
1. If the solution θ is constant, and θ ≡ θ0 , then the model structure is locally identifiable at θ0 .
2. If the solution θ is constant, and θ 6≡ θ0 , then the model structure is locally identifiable at θ and not globally identifiable at θ0 .
3. If the solution θ is time-varying, then the current solution has been introduced by
the preprocessing step.
4. If the DAE solver indicates that existing solutions are not locally unique, then the
model structure is either not locally identifiable at θ0 or new solutions have been
introduced by the preprocessing step.
5. If the DAE solver indicates that no solutions exist, numerical problems have occurred.
In case 4, further examination of the identifiability properties should be done using
Result 7.2 discussed below.
126
7.3.3
7
Simulation-Based Tests for Identifiability
Identifiable Functions of Parameters
If a model structure is not locally identifiable, it may be interesting to examine if some
parameters or functions of parameters are identifiable. This makes it possible to prove
that a model structure is not locally identifiable at a certain parameter value. It may also
be interesting to know which parameters that are identifiable if they represent physical
quantities.
The basic observation that is used to find identifiable functions of parameters is that
if a model structure is not identifiable, then it should be possible to make it identifiable
by fixing one or more parameters. If for example a + b is identifiable but not a and
b, then b can be made identifiable by fixing a to, e.g., a = 1. As it is not possible to
know beforehand which parameters that can be fixed, all parameters and combinations
of parameters have to be tried until the model structure becomes identifiable. For each
parameter (each element of θ) it is tested if the model becomes identifiable with this
parameter fixed to the value of the corresponding element in θ0 . This is checked by
restarting the procedure from the preprocessing step. If it cannot be made identifiable by
fixing one parameter, then combinations of two parameters are tested, then combinations
of three parameters, and so on.
When a parameter, or combination of parameters, that when fixed makes the model
structure identifiable has been found, it is still necessary to show that the model actually is
not identifiable. This is because the extra solutions that were reported by the DAE solver
may be a result of the preprocessing step. To prove this, the value of the parameters are
changed from their corresponding values in θ0 . If the simulation procedure still gives
constant values for all parameters, it has been proven that the model structure is not globally identifiable since there are more than one set of parameter values that give the same
input-output behavior. Local identifiability can be tested by making small changes in the
parameters.
When some parameters that can be fixed have been found, it may be interesting to
examine how the values of these parameters affect the value of the other parameters. This
can be done by varying the fixed parameters to different fixed values, and noting the
values of the other parameters. In this way it is possible to determine which function of
the parameters that is identifiable.
This discussion leads to the following result.
Result 7.2
Consider a modified identifiability problem with the parameter vector divided into two
parts (θ1 and θ2 ) where the first part θ1 is considered known,
gi (u, y, x, ẋ, θ0,1 , θ0,2 ) = 0
˙ θ1 , θ 2 ) = 0
gi (u, y, x̃, x̃,
θ̇2 = 0.
i = 1, 2, . . . , r
(7.9a)
i = 1, 2, . . . , r
(7.9b)
(7.9c)
If simulation of the preprocessed version of this problem with θ1 6= θ0,1 gives a constant
θ2 , then the problem is not globally identifiable atθ0 . Furthermore, the identifiable functions of the parameter are defined by θ2 − f (θ1 ) where the function f (θ1 ) is defined as
the value of θ2 when simulating the preprocessed version of (7.9) for a certain θ1 .
7.4
127
Example
When using this method, it is as discussed above first necessary to find the parameters
that can be varied and thus should be included in θ1 . This is done by first trying each
parameter, then combinations of two parameters and so on. When a set of parameters θ1
that makes the model identifiable when fixed have been found, the identifiable functions
of the parameters are computed by changing the value of θ1 and noting the value of θ2 .
7.4
Example
In this section, the identifiability checking procedure is exemplified on a compartmental
model.
Example 7.2: Compartmental model
In this example the following model structure from Ljung and Glad (1994) is studied:
ẋ(t) = −
Vm x(t)
− k01 x(t)
km + x(t)
(7.10a)
x(0) = D
(7.10b)
y(t) = cx(t)
(7.10c)
Let the initial condition D be known, so that the unknown parameters are
 
Vm
 km 

.
θ= 
k01 
c
(7.11)
Assume that identifiability is to be tested at
 
1
2 

θ0 = 
3  .
4
(7.12)
The basic setup to be simulated (7.8) is then
1 · x(t)
− 3 · x(t)
2 + x(t)
y(t) = 4 · x(t)
ẋ(t) = −
(7.13a)
(7.13b)
Vm (t)x̃(t)
˙
x̃(t)
=−
− k01 (t)x̃(t)
km (t) + x̃(t)
y(t) = c(t) · x̃(t)
(7.13d)
T
θ(t) = Vm (t) km (t) k01 (t) c(t)
(7.13e)
θ̇(t) = 0
(7.13f)
(7.13c)
In the preprocessing step, (7.13f) should be removed, and nθ − ny = 3 new equations
should be added by differentiating equations. Here, (7.13c) is differentiated twice and
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Simulation-Based Tests for Identifiability
8
km
7
c
6
5
4
3
2
1
0
1
1.5
2
2.5
Vm
3
3.5
4
Figure 7.1: Identifiable functions of parameters.
(7.13d) is differentiated once to get three new equations. (7.13c) is chosen to be differentiated twice since it contains several parameters. The initial value is set to x(0) = x̃(0) = 1.
Simulating the new system using the DAE solver in Dymola gives
 
1
2

θ(t) ≡ 
(7.14)
3 ,
4
so the model structure is locally identifiable at this parameter value.
On the contrary to what was done above, assume now that identifiability should be
examined for the case when the initial condition x(0) is unknown. In this special case
it is possible to see that the model structure is not identifiable without going through the
preprocessing step again. This is done by simulating the same system as above but with
x(0) 6= x̃(0). Doing this gives a constant θ(t), but with θ(t) 6= θ0 . This directly shows
that the model structure is not identifiable with unknown initial conditions. To examine
which functions of the parameters that are identifiable, several different values of x̃(0) are
tried, and for each case the value of θ(t) is noted. During this procedure k01 (t) is always
at its true value, k01 (t) = 3, so this parameter is identifiable. The other parameters vary
when x̃(0) is varied, so they are not identifiable. To illustrate which functions of the
parameters that are identifiable, km and c are plotted against Vm in Figure 7.1. The figure
suggests that Vm /km and Vm · c are identifiable.
7.5
Conclusions and Ideas For Extensions
7.5
129
Conclusions and Ideas For Extensions
In this chapter we have discussed the possibility to examine identifiability of parameterized models. The basic idea is to simulate the system with the parameters as unknown
variables and examine if there is more than one solution. However, available DAE solvers
typically cannot handle the posed problems directly. Because of this a preprocessing step
was discussed.
The preprocessing step, as it is described here, is heuristic and may require manual
intervention. Further research efforts could thus be put at making this process fully automatic.
An interesting aspect of the method in this chapter is that DAE solvers make it possible
to draw certain conclusions about dynamic systems. This could be applied to other areas
than identifiability. Some possibilities are discussed below.
7.5.1
Initialization for Identification
For nonlinear models and linear models where the parameters enter nonlinearly, the system identification problem usually has to be solved as a non-convex optimization problem.
This means that it is important to have a good initial guess for the parameters to avoid local minima. Perhaps the method described in this chapter could be used to solve this
problem by replacing (7.8a) with measured u and y or a black-box model, similarly to
what was done by Parrilo and Ljung (2003).
7.5.2
Non-Minimum Phase Systems
Linear non-minimum phase systems are characterized by, among other things, that the
transfer function from output to input is unstable. This can easily be checked with a DAE
solver by inverting the model (making outputs inputs and vice versa). Inverting the model
is easy with a DAE solver since the model must not be written in, e.g., state-space form.
All that has to be done is to specify the (original) output as a function of time, simulate the
system and observe the resulting (original) input. This procedure also works for nonlinear
systems.
7.5.3
Trajectory Generation
For robotic applications, trajectory generation is a common problem. Trajectory generation basically means that a control input is calculated from a desired path by inversion
of a model of the robot. The inversion is usually simple to perform when the dynamics
of the model are not too complicated. However, for complicated models one method to
invert the model could be to use a DAE solver.
7.5.4
Observability
The problem of nonlinear observability is to examine if the internal variables, e.g., x(t),
of a system can be computed given measurements of the inputs and outputs. This problem
is similar to the identifiability problem, and could thus also be possible to examine using
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a DAE solver. Assume that the model F (ẋ(t), x(t), y(t), u(t)) = 0 should be checked
for observability. This can be formulated as examining if the following DAE is uniquely
solvable for unknown initial conditions on x̃(t):
F (ẋ(t), x(t), y(t), u(t)) = 0
˙
F (x̃(t),
x̃(t), y(t), u(t)) = 0
x(0) = x0
(7.15a)
(7.15b)
(7.15c)
The preprocessing step would here be somewhat different than it was for the identifiability
case.
Part II
Linear DAE Models
131
8
Linear SDAE Models
In this chapter we discuss noise modeling in linear DAE systems.
8.1
Introduction
In Chapter 4, we discussed how noise models can be included in nonlinear DAE systems,
and how this can be used to estimate internal variables and unknown parameters. In
the case of linear DAE models, a more thorough analysis can be performed than for the
nonlinear case, and simpler conditions for well-posedness can be derived. In the present
chapter we will therefore discuss conditions that need to be satisfied to make it possible
to interpret a linear SDAE with white noise inputs as an SDE, and in the following two
chapters we will discuss conditions for well-posedness of parameter estimation and state
estimation problems.
For continuous-time linear state-space models, a noise model can be added according
to
ẋ(t) = Ax(t) + B1 u(t) + B2 v1 (t)
(8.1a)
y(t) = Cx(t) + v2 (t),
(8.1b)
where v1 (t) and v2 (t) are white noise signals. As discussed in Section 2.7 this description
should be interpreted as a stochastic integral. To point this out, the notation
dx = Axdt + B1 u(t)dt + B2 dv1
(8.2a)
dy = Cxdt + dv2
(8.2b)
can be used. A Kalman filter (Anderson and Moore, 1979; Kailath et al., 2000) can then
be implemented to estimate the state and predict future state values and outputs. We will
here discuss what measures that need to be taken to use similar methods for linear DAE
133
134
8
Linear SDAE Models
systems. As discussed previously, it is possible to transform a linear DAE into state-space
form, so it will in principle be possible to use the same methods as for state-space systems.
However, there are issues on the well-posedness of DAE systems with noise models. This
will be discussed below.
8.2
Noise Modeling
The natural approach to add noise to a linear DAE model is of course according to the
linear SDAE shown on page 13,
E ẋ(t) = Jx(t) + K1 u(t) + K2 v1 (t)
y(t) = Lx(t) + v2 (t),
(8.3a)
(8.3b)
where v1 (t) represents the unmeasured inputs and and v2 (t) represents the measurement
noise. K2 is a constant matrix. This is analogous to how noise is added in a state-space
model, see (8.1). It can be realized from the discussion in Section 2.3, that the internal
variables x(t) can depend on derivatives of v1 (t). But if v1 (t) is white noise, the derivative is not well-defined (see Section 2.7), so then the internal variables cannot depend on
derivatives of v1 (t). To see how this can happen, consider the following example:
Example 8.1: Linear SDAE
Consider the linear SDAE
ẋ1 (t)
1 0
x1 (t)
−1
0 0
=
+
v1 (t)
ẋ2 (t)
0 1
x2 (t)
0
1 0
x1 (t)
+ v2 (t).
y(t) = 0 1
x2 (t)
(8.4a)
(8.4b)
The first equation states that
x1 (t) = v1 (t)
(8.5)
which inserted into the second equation gives
x2 (t) = ẋ1 (t) = v̇1 (t).
(8.6)
If v1 (t) is white noise, this is questionable since the derivative of white noise is not welldefined. Furthermore, (8.5) is also questionable if x1 (t) is a physical variable since a
time-continuous white noise process has infinite variance.
In this section we derive conditions on the matrix K2 which guarantee that x(t) does
not depend on derivatives of v1 (t). Two equivalent conditions are derived, one using time
domain methods (Section 8.2.1) and one using frequency domain methods (Section 8.2.2).
The condition that x(t) does not depend on derivatives can be seen as a basic required
condition on SDAE models to make it possible to interpret them as an SDE, since the
derivative of white noise is not well-defined. To estimate the unknown parameters and
the internal variables, further conditions must be imposed on the model. This will be
discussed in the following chapters.
8.2
135
Noise Modeling
8.2.1
Time Domain Derivation
In this section we use time domain methods to derive a condition on K2 which is equivalent to that derivatives of v1 (t) do not affect x(t).
Consider (8.3). We can rewrite the equations as
E ẋ(t) = Jx(t) + K1
K2
u(t)
v1 (t)
(8.7a)
y(t) = Lx(t) + v2 (t).
(8.7b)
If we now consider the vector
u(t)
v1 (t)
(8.8)
as the input and assume that the system is regular, we know from Lemma 2.3 that there
exist transformation matrices P and Q such that the transformation
−1
P EQQ
−1
x(t) + P K1
K2
0
B1
−1
Q x(t) +
I
D1
B2
D2
ẋ(t) = P JQQ
u(t)
v1 (t)
(8.9)
gives the system
I
0
0
N
−1
Q
ẋ(t) =
A
0
u(t)
v1 (t)
(8.10)
where N is a nilpotent matrix. Furthermore, Theorem 2.3 gives that the solution can be
described by
ẋ1 (t) = Ax1 (t) + B1 u(t) + B2 v1 (t)
(8.11a)
x2 (t) = −D1 u(t) − D2 v1 (t)
−
m−1
X
N i D1 u(i) (t) −
i=1
x1 (t)
= Q−1 x(t)
x2 (t)
x1 (t)
y(t) = LQ
+ v2 (t).
x2 (t)
m−1
X
(i)
N i D2 v1 (t)
(8.11b)
i=1
(8.11c)
(8.11d)
When we have a state-space description, v1 (t) and v2 (t) are white noise signals. If
they were not white noise, we would technically not have a state-space description since
future noise values then would depend on the current noise value. To be able to transform (8.3) into state-space form we would like to allow that v1 (t) and v2 (t) are white
noise also here. As discussed in Section 2.7, time-continuous white noise signals require
careful treatment. Most importantly, we cannot allow that any derivatives of v1 (t) occur
in (8.11). If m = 1 this requirement is trivially fulfilled and (8.11) is equivalent to the
136
8
Linear SDAE Models
state-space description
ẋ1 (t) = |{z}
A x1 (t) + B1 u(t) + B2 v1 (t)
|{z}
|{z}
Ã
B̃1
(8.12a)
B̃2
0
0
I
x1 (t) + LQ
u(t) + LQ
v (t) + v2 (t).
y(t) = LQ
−D1
−D2 1
0
|
{z
}
|
{z
}
| {z }
C̃
D̃
(8.12b)
Ñ
However, if m > 1, 8.11b gives that we have to require
N D2 = 0
(8.13)
to avoid differentiation of v1 (t).
Note that (8.13) is related to the impulse controllability with respect to v1 (t), see for
example the book by Dai (1989b) or the original paper by Cobb (1984). If the system were
impulse controllable with respect to v1 (t), as many derivatives of it as possible would be
included. What we need is actually the opposite of impulse controllability with respect to
v1 (t).
The requirement (8.13) may seem difficult to check in the original model (8.3), but in
the following theorem we show that it is equivalent to the matrix K2 being in the range
of a certain matrix. This makes it possible to avoid derivatives of the noise already at the
modeling stage. To formulate the theorem, we need to consider the transformation (8.9)
with matrices P and Q which gives a system in the form (8.10). Let the matrix N have
the singular value decomposition
T
Σ 0
Σ 0
T
V1 V2 ,
N =U
(8.14)
V =U
0 0
0 0
where V2 contains the last k columns of V having zero singular values. Finally, define the
matrix M as
I 0
M = P −1
.
(8.15)
0 V2
It is now possible to derive a condition on K2 .
Theorem 8.1
The condition (8.13) is equivalent to
K2 ∈ V(M )
(8.16)
where V(M ) denotes the range of the matrix M , K2 is defined in (8.3) and M is defined
in (8.15).
The expression (8.16) means that K2 is in the range of M , that is the columns of K2
are linear combinations of the columns of M .
Proof: From Lemma 2.3 we know that there exist matrices P and Q such that
u(t)
−1
−1
K
K
P EQQ ẋ(t) = P JQQ x(t) + P
1
2
v1 (t)
(8.17)
8.2
137
Noise Modeling
gives the canonical form
I 0
A
−1
Q ẋ(t) =
0 N
0
0
B1
−1
Q x(t) +
I
D1
B2
D2
u(t)
.
v1 (t)
(8.18)
Note that K2 can be written as
K2 = P −1
B2
.
D2
Let the matrix N have the singular value decomposition
Σ 0
N =U
VT
0 0
(8.19)
(8.20)
where Σ is a diagonal matrix with nonzero elements. Since N is nilpotent it is also
singular, so k singular values are zero. Partition V as
V = V1 V2 ,
(8.21)
where V2 contains the last k columns of V having zero singular values. Then N V2 = 0.
We first prove the implication (8.16) ⇒ (8.13): Assume that (8.16) is fulfilled. K2
can then be written as
S
S
S
0
−1
−1 I
=P
(8.22)
K2 = M
=P
V2 T
T
T
0 V2
for some matrices S and T . Comparing with (8.19), we see that B2 = S and D2 = V2 T .
This gives
N D2 = N V 2 T = 0
(8.23)
so (8.13) is fulfilled.
Now the implication (8.13) ⇒ (8.16) is proved: Assume that (8.13) is fulfilled. We
then get
T
Σ 0
V1
ΣV1T D2
0 = N D2 = U
D
=
U
.
(8.24)
2
0 0
0
V2T
This gives that
V1T D2 = 0,
(8.25)
so the columns of D2 are orthogonal to the columns of V1 , and D2 can be written as
D2 = V2 T
for some matrix T . Equation (8.19) now gives
B2
B2
I
K2 = P −1
= P −1
= P −1
D2
V2 T
0
(8.16) is fulfilled.
(8.26)
0
V2
B2
B2
=M
∈ V(M ).
T
T
(8.27)
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8
Linear SDAE Models
We now consider how an SDAE can be transformed into state-space form. If it is
assumed that the matrix K2 in (8.3) is such that (8.13), or equivalently (8.16), is fulfilled,
the form (8.11) can be written as
ẋ1 (t) = Ax1 (t) + B1 u(t) + B2 v1 (t)
x2 (t) = −D1 u(t) − D2 v1 (t) −
m−1
X
(8.28a)
N i D1 u(i) (t)
(8.28b)
i=1
x1 (t)
= Q−1 x(t)
x2 (t)
x (t)
+ v2 (t).
y(t) = LQ 1
x2 (t)
(8.28c)
(8.28d)
We now proceed to transform (8.28) into a state-space description with u(m−1) (t) as
the input using the same method as in Section 2.3.5. We thus define x3 (t) according
to (2.125), which gives the description
ẋ1 (t) = Ax1 (t) + B1
x2 (t) = − D1
N D1
. . . 0 x3 (t) + B2 v1 (t)
. . . N m−2 D1 x3 (t)
0
m−1
−N
D1 u(m−1) (t) − D2 v1 (t)
 


0
0 I ... 0
 .. 
 .. .. . .
.. 
 

. .
ẋ3 (t) =  . .
 x3 (t) +  .  u(m−1) (t)
0
0 0 . . . I 
I
0 0 ... 0
x (t)
+ v2 (t).
y(t) = LQ 1
x2 (t)
(8.29a)
(8.29b)
(8.29c)
(8.29d)
Eliminating x2 (t) and stacking x1 (t) and x3 (t) together now gives the description

A

0
ẋ1 (t)

=  ...
ẋ3 (t)

0
0
|
B1
0
..
.
0
I
..
.
0
0
...
...
..
.
0 ...
0 ...
{z

 
0
0
0
0
 

..  x1 (t) +  ..  u(m−1) (t) + B2 v
.
1
.
0
 x3 (t)
 
(8.30a)
0
| {z }
I
B̃2
0
I
| {z }
}
Ã
I
y(t) = LQ
0
|
0
−D1
B̃1
0
−N D1
{z
...
0
. . . −N m−2 D1
x1 (t)
+
x3 (t)
}
C̃
0
0
(m−1)
LQ
u
(t)
+
LQ
v (t) + v2 (t).
−D2 1
−N m−1 D1
{z
}
|
{z
}
|
D̃
Ñ
(8.30b)
8.2
139
Noise Modeling
Defining
z(t) =
x1 (t)
x3 (t)
(8.31)
gives the more compact notation
ż(t) = Ãz(t) + B̃1 u(m−1) (t) + B̃2 v1 (t)
v1 (t)
y(t) = C̃z(t) + D̃u(m−1) (t) + Ñ I
.
v2 (t)
(8.32a)
(8.32b)
If v1 and v2 are white noise signals, then this description should be interpreted as a
stochastic integral. To point this out, the notation
dz = Ãzdt + B̃1 u(m−1) dt + B̃2 dv1
dv1
(m−1)
dy = C̃zdt + D̃u
dt + Ñ I
dv2
(8.33a)
(8.33b)
can be used. We have shown that it is possible to construct a state-space system with a
noise model that describes the behavior of the linear DAE system with noise model (8.3)
if N D2 = 0 holds. However, the internal variables and the measured output may be white
noise processes, see e.g., (8.28). This issue will be discussed in the following chapters.
Note that in the state-space model, the noise on the output equation is in general
correlated with the noise on the state equation through the v1 (t) term. This correlation is
eliminated if D2 = 0. Then Ñ = 0 so the state-space description simplifies to
ż(t) = Ãz(t) + B̃1 u(m−1) (t) + B̃2 v1 (t)
(8.34a)
y(t) = C̃z(t) + D̃u(m−1) (t) + v2 (t).
(8.34b)
Here, the noise on the state and output equations are correlated only if v1 (t) and v2 (t)
are.
8.2.2
Frequency Domain Derivation
In the previous section, Theorem 8.1 gave a condition on how noise can be added to a
linear DAE system without making the internal variables of the system depend on derivatives of the noise. The criterion was based on a canonical form. As will be shown in
this section, an equivalent result can also be derived in the frequency domain without
requiring calculation of the canonical form.
Instead of examining if
N D2 = 0
(8.35)
to avoid derivatives of the noise, we will here examine if the transfer function from the
process noise to the internal variables is proper (i.e., does not have higher degree in the
numerator than the denominator). These two conditions are equivalent, since a transfer
function differentiates its input if and only if it is non-proper. Consider the linear DAE
system
E ẋ(t) = Jx(t) + K1 u(t) + K2 v1 (t)
y(t) = Lx(t) + v2 (t).
(8.36a)
(8.36b)
140
8
Linear SDAE Models
The question is if the transfer function
G(s) = (sE − J)−1 K2
(8.37)
is proper. Note that we want to examine if the internal variables x depend on derivatives
of the noise, so L is not included in the transfer function.
Throughout the section, some concepts from the theory of matrix fraction descriptions
(MFD) will be needed. MFDs are discussed for example by Kailath (1980) and by Rugh
(1996) where they are called polynomial fraction descriptions.
We start by defining the row degree of a polynomial matrix and the concept of a row
reduced polynomial matrix according to Rugh (1996, page 308).
Definition 8.1 (Row degree). The i:th row degree of a polynomial matrix P (s), written
as ri [P ], is the degree of the highest degree polynomial in the i:th row of P (s).
Definition 8.2 (Row reduced). If the polynomial matrix P (s) is square (n × n) and
nonsingular, then it is called row reduced if
deg[det P (s)] = r1 [P ] + · · · + rn [P ].
(8.38)
We will also need the following theorem from Kailath (1980):
Theorem 8.2
If the n × n polynomial matrix D(s) is row reduced, then D−1 (s)N (s) is proper if and
only if each row of N (s) has degree less than or equal the degree of the corresponding
row of D(s), i.e., ri [N ] ≤ ri [D], i = 1, . . . , n.
Proof: See Kailath (1980, page 385).
We will examine if the transfer function (8.37) (which actually is a left MFD) fulfills
the conditions of Theorem 8.2. According to Rugh (1996, page 308) a MFD can be
converted into row reduced form by pre-multiplication of a unimodular1 matrix U (s).
More specifically, with
D(s) = U (s)(sE − J)
(8.39a)
N (s) = U (s)K2 ,
(8.39b)
and consequently
D−1 (s)N (s) = (sE − J)−1 K2 = G(s),
(8.40)
D(s) is row reduced for a certain unimodular matrix U (s). U (s) is not unique, it can
for example be scaled by a constant. However, Theorem 8.2 shows that for each choice
of U (s), the transfer function G(s) of the system is proper if the highest degree of the
polynomials in each row in N (s) is lower than or equal to the highest degree of the
polynomials in the corresponding row of D(s). This gives a condition on K2 in the
following way:
1 A polynomial matrix is called unimodular if its determinant is a nonzero real number (Rugh, 1996,
page 290).
8.3
141
Example
Writing U (s) as
U (s) =
m
X
Ui si
(8.41)
i=0
and writing the j:th row of Ui as Uij , shows that the condition
Uij K2 = 0 i > rj [D], j = 1 . . . n
(8.42)
guarantees that the transfer function G(s) of the system is proper. Here, n is the size of
the square matrices E and J, or equivalently the number of elements in the vector x(t).
Conversely, assume that (8.42) does not hold. Then some row degree of N (s) is
higher than the corresponding row degree of D(s), so the transfer function G(s) is then
according to Theorem 8.2 not proper. This discussion proves the following theorem.
Theorem 8.3
Consider the transfer function G(s) = (sE − J)−1 K2 where the matrices E and J are
n × n. Let U (s) be a unimodular matrix such that D(s) = U (s)(sE − J) is row reduced.
Write U (s) as
m
X
U (s) =
Ui si
(8.43)
i=0
and let Uij be the j:th row of Ui . Then G(s) is proper if and only if
Uij K2 = 0 i > rj [D], j = 1, . . . , n.
(8.44)
Note that the criterion discussed in this section requires that the MFD is transformed
into row reduced form. An algorithm for finding this transformation is provided by Rugh
(1996, Chapter 16).
We have now proved two theorems, one using time domain methods and one using
frequency domain methods, that give conditions which are equivalent to that v1 (t) is not
differentiated. This means that these two conditions are equivalent as well.
8.3
Example
In this section the results of the previous section are exemplified on a simple physical
DAE system. We will use Theorem 8.1 and 8.3 to examine how a noise model can be
added to a system consisting of two rotating masses as shown in Figure 8.1. It will be
M1
ω1
M2
M3
ω2
M4
Figure 8.1: Two interconnected rotating masses.
shown that noise can only be added in equations where it can be physically motivated.
142
8
Linear SDAE Models
The system is described by the torques M1 (t), M2 (t), M3 (t) and M4 (t) and the angular
velocities ω1 (t) and ω2 (t). The masses have the moments of inertia J1 and J2 . The
equations describing this system are
J1 ω̇1 (t) = M1 (t) + M2 (t)
(8.45a)
J2 ω̇2 (t) = M3 (t) + M4 (t)
(8.45b)
M2 (t) = −M3 (t)
(8.45c)
ω1 (t) = ω2 (t).
(8.45d)
where (8.45a) and (8.45b) describe the angular accelerations the torques produce, and
(8.45c) and (8.45d) describe how the two parts are connected. Written in DAE form,
these equations are

J1
0

0
0
0
J2
0
0


ω̇1 (t)
0 0


0 0
  ω̇2 (t)  =
0 0 Ṁ2 (t)
0 0
Ṁ3 (t)

0 0 1
0 0 0

 0 0 −1
−1 1 0

 
0
ω1 (t)
1

 
1
  ω2 (t)  + 0
−1 M2 (t) 0
0
0
M3 (t)

0 1
 M1 (t)
0 M4 (t)
0
(8.46)
if M1 (t) and M4 (t) are considered as inputs. Using the transformation matrices


P =

1
0
0
2
− J1J+J
2
1
J1 +J2
 1
 J1 +J2

Q=
0
0
1
0
0
1
0
−1
J1
J1 +J2
J2
J1 +J2
1
− J1J+J
2
0
0
2
− J1J+J
2

0 0
0 0

1 −1
0

0
−1

0
0
(8.47)
(8.48)
1
the DAE system can be transformed into the canonical form (2.95) of Lemma 2.3. The
transformation


ω1 (t)
 ω2 (t) 

z(t) = Q−1 
M2 (t)
M3 (t)
(8.49)
8.3
143
Example
gives

1
0

0
0
0
0
0
J2
− JJ11+J
2

0 0
0 0
 ż(t) =
0 0
0 0

0 0 0
0 1 0

0 0 1
0 0 0


1
0
 0
0
 z(t) + 
 0
0
2
− J1J+J
1
2
1
0
0
J1
J1 +J2

 M1 (t)

 M4 (t) . (8.50)
If we now want to incorporate noise into the DAE (8.46) by adding K2 v1 (t) to the right
hand side of (8.46), which K2 -matrices are allowed? To answer this question Theorem 8.1
can be used. We begin by calculating the matrices P −1 and V2 from (8.47) and (8.50).
We have that




0
0 0
0 0
0 0 ⇒ V2 = 1 0
(8.51)
N = 0
J2
0
0
− JJ11+J
0
1
2
and that
J1
J1 +J2
 J2
 J1 +J2

P −1 = 
0
0
0
0
0
−1
1
0
−1
0

−1
1
.
0
0
The condition of Theorem 8.1 can now be calculated:
 J 1
J1 +J2
 J 2
I 0
 J1 +J2
K2 ∈ V P −1
=V
 0
0 V2
0
1
0
−1
0
(8.52)

−1

1


0 
0
(8.53)
This simply means that white noise cannot be added to equation (8.45d) (if J1 > 0 and
J2 > 0). We will comment on this result below, but first we show how to derive the same
condition using the frequency domain method in Theorem 8.3. Transforming the system
into row reduced form gives (assuming J1 > 0 and J2 > 0)

 1
− J1 J12 0 s
 0
1 0 0

(8.54)
U (s) = 
 0
0 1 0
0
0 0 1
 1
 

− J1 J12 0 0
0 0 0 1
 0


1 0 0
 + 0 0 0 0  s
=
(8.55)
 0


0 0 0 0
0 1 0
0 0 0 0
0
0 0 1
{z
}
|
{z
} |
U0
U1
144
8
Linear SDAE Models
and

0
0
D(s) = 
0
1
0
J2 s
0
−1
1
J1
0
1
0

− J12
−1 

1 
0
(8.56)
with notation from section 8.2.2.
The row degrees of D(s) are r1 [D] = 0, r2 [D] = 1, r3 [D] = 0, and r4 [D] = 0.
Theorem 8.3 shows that the transfer function is proper if and only if


0 0 0 1
0 0 0 0 K2 = 0.
(8.57)
0 0 0 0
What equation (8.57) says is that the last row of K2 must be zero, which is the same
conclusion as was reached using the time domain method, Theorem 8.1.
The result that white noise cannot be added to the equation
ω1 (t) = ω2 (t)
(8.58)
is a result that makes physical sense since this equation represents a rigid connection.
Furthermore, a noise term added to this equation would require at least one of ω1 and ω2
to make instantaneous changes. The equations
J1 ω̇1 (t) = M1 (t) + M2 (t)
(8.59)
J2 ω̇2 (t) = M3 (t) + M4 (t)
(8.60)
show that at least one of the torques Mi (t) would have to take infinite values. This is of
course not physically reasonable. Consequently, the Theorems 8.1 and 8.3 tell us how to
add noise in a physically motivated way, at least for this example. They could therefore
be used to guide users of object-oriented modeling software on how noise can be added
to models.
8.4
Sampling with Noise Model
Also when we have a noise model, it is interesting to examine what the sampled description of a linear DAE system is. We will use Lemma 2.5 to derive the sampled counterpart
of the SDAE system
E ẋ(t) = Jx(t) + K2 v1 (t)
(8.61a)
y(t) = Lx(t) + v2 (t).
(8.61b)
To simplify the discussion we examine the case without input signal. A system with
input signal can be handled according to what was discussed in Section 2.3.6. The noise
signals v1 (t) and v2 (t) are interpreted as Wiener processes dv1 and dv2 with incremental
8.4
145
Sampling with Noise Model
covariances
E dv1 dv1T = Q1 dt
E dv1 dv2T = Q12 dt
E dv2 dv2T = Q2 dt.
(8.62a)
(8.62b)
(8.62c)
If K2 is such that v1 (t) is not differentiated, we know from Section 8.2.1 that (8.61) can
be transformed into the SDE
dz = Ãzdt + B̃dv1
| {z }
(8.63a)
dṽ1 (t)
dy = C̃zdt + Ñ
|
dv1
.
I
dv2
{z
}
(8.63b)
dṽ2
The incremental covariances of the Wiener processes ṽ1 and ṽ2 are
E dṽ1 dṽ1T = R1 dt = B̃Q1 B̃ T dt
Ñ T
T
E dṽ1 dṽ2 = R12 dt = B̃ Q1 Q12
dt
I
T
Q1 Q12
Ñ
dt.
E dṽ2 dṽ2T = R2 dt = Ñ I
QT12 Q2
I
(8.64a)
(8.64b)
(8.64c)
Since R1 , R12 , and R2 are known for the state-space model (8.63), a sampled version of
the original DAE system (8.61) can now be calculated using Lemma 2.5. We get that a
sampled version of (8.61) is
z(Ts k + Ts ) = Φz(Ts k) + v̄(Ts k)
(8.65a)
y(Ts k + Ts ) − y(Ts k) = θz(Ts k) + ē(Ts k)
(8.65b)
with
Φ = eÃTs
ZTs
θ = C̃
0
(8.66a)
eÃτ dτ
(8.66b)
146
8
Linear SDAE Models
and
T
E v̄(t)v̄ (t) = R̄1 =
ZTs
T
eÃ(Ts −τ ) R1 eÃ(Ts −τ ) dτ
(8.67a)
0
T
E v̄(t)ē (t) = R̄12 =
ZTs
eÃ(Ts −τ ) R1 ΘT (τ ) + R12 dτ
(8.67b)
0
E ē(t)ēT (t) = R̄2 =
ZTs
T
Θ(τ )R1 ΘT (τ ) + Θ(τ )R12 + R12
ΘT (τ ) + R2 dτ (8.67c)
0
ZTs
Θ(τ ) = C̃
eÃ(s−τ ) ds.
(8.67d)
τ
When the measurements are sampled, it may seem awkward to first define a continuoustime measurement equation and then sample it as was proposed in this section. It is
possible to instead define a discrete-time measurement equation, and this approach will
be discussed in Chapters 9 and 10.
8.5
Kalman Filtering
We have now established how to transfer a linear DAE system into a discrete-time statespace system which gives an equivalent description of the output at the sampling instants.
This opens up the possibility to use a discrete-time Kalman filter to estimate the states
and make predictions. To be concrete, assume that we have arrived at the discrete-time
state-space model
z(Ts k + Ts ) = Az(Ts k) + Bu(Ts k) + N v1 (Ts k)
y(Ts k) = Cz(Ts k) + Du(Ts k) + v2 (Ts k).
(8.68a)
(8.68b)
The implementation of a Kalman filter is then straightforward (e.g., Anderson and Moore,
1979; Kailath et al., 2000). We could also use the continuous-time state-space description (8.12) or (8.32) and implement a continuous-time Kalman filter. Note that implementation of a continuous-time Kalman filter with digital hardware always involves some sort
of approximation since digital hardware operates in discrete-time.
We only get estimates of the state vector x1 (t) and the output y(t), not of complete
vector of internal variables x(t), through a normal Kalman filter. The vector x(t) may not
even have finite variance. This can be realized from (8.28) since x2 (t) can be equal to
a white noise process. In the following chapters we will therefore discuss how it can be
guaranteed that all variables of interest have finite variance.
8.6
Time-Varying Linear SDAE Models
It is also interesting to examine when a time-varying linear DAE with a white noise input
is well-defined so that its input-output behavior can be interpreted as a SDE. In this section
8.6
Time-Varying Linear SDAE Models
147
we will develop a parallel result to what was done for time-invariant linear DAE systems
previously in the chapter. Consider a time-varying linear DAE as discussed in Section 2.4,
E(t)ẋ(t) = A(t)x(t) + f (t)
y(t) = C(t)x(t).
(8.69a)
(8.69b)
We will assume that there is a deterministic input u(t) and a white noise input v1 (t) so
that
f (t) = K1 (t)u(t) + K2 (t)v1 (t).
(8.70)
There is also white measurement noise v2 (t). The time-varying linear SDAE can then be
written as
E(t)ẋ(t) = A(t)x(t) + K1 (t)u(t) + K2 (t)v1 (t)
y(t) = C(t)x(t) + v2 (t).
(8.71a)
(8.71b)
As with the time-invariant case, the problem is that derivatives of the noise process v1 (t)
might appear, and these are not well-defined. This can be realized from the transformations in Section 2.4 from which we get that (8.71a) is equivalent to
ẋ1 (t) = A13 (t)x3 (t) + f1 (t)
(8.72a)
0 = x2 (t) + f2 (t)
(8.72b)
0 = f3 (t)
(8.72c)
where


f1 (t)
f2 (t) = Pm+1 (t)P̃m (t, d ) · · · P̃1 (t, d ) K1 (t)u(t) + K2 (t)v1 (t)
dt
dt
f3 (t)
(8.73)
with Pi , P̃i defined as in Section 2.4. Since derivatives of white noise are not well-defined,
K2 (t) must be such that v1 (t) is not differentiated. It must also be assumed that K1 (t)u(t)
is sufficiently differentiable. We will also assume that the DAE is regular so that x3 and
f3 are of size zero. This is a parallel to the regularity assumption for linear time-invariant
DAE systems. The conditions for when the input-output behavior of a time-varying linear
SDAE is well-defined is given by the following proposition.
Proposition 8.1
Let the matrices Pi for the time-varying linear SDAE (8.69) be defined as in Section 2.4
and the assumptions in Theorem 2.6 hold. Also assume that (8.69) is regular so that the
size of x3 is zero. Then the internal variables are not affected by derivatives of the noise
process v1 (t) if and only if
0 0 0 Isi 0 Pi (t)Pi−1 (t) · · · P1 (t)K2 (t) = 0 i = 1, . . . , m,
(8.74)
that is the fourth block row of Pi (t)Pi−1 (t) · · · P1 (t)K2 (t) is zero for i = 1, . . . , m where
the division into block rows for each matrix Pi is done according to the division in Theorem 2.5.
148
8
Linear SDAE Models
Proof: We have that


f1 (t)
f2 (t) = Pm+1 (t)P̃m (t, d ) · · · P̃1 (t, d ) K1 (t)u(t) + K2 (t)v1 (t)
dt
dt
f3 (t)
where

I
0

P̃i = 
0
0
0
0
I
0
0
0
0
0
I
0
0
d
dt I
0
0
I
0

0
0

0
 Pi (t).
0
I
(8.75)
(8.76)
Since all the matrices Pi are invertible, (8.74) is a necessary and sufficient condition to
avoid differentiation of the noise process v1 (t),
If the conditions of the theorem are satisfied we can thus write a time-varying linear
SDAE as
d
B2 (t)
0
B1 (t, dt
ẋ1 (t)
)
v (t)
(8.77)
u(t) +
=
+
d
D2 (t) 1
x2 (t)
0
D1 (t, dt
)
where
d
d
d
B1 (t, dt
)
= Pm+1 (t)P̃m (t, ) · · · P̃1 (t, )K1 (t)
d
D1 (t, dt )
dt
dt
B2 (t)
= Pm+1 (t)Pm (t) · · · P1 (t)K2 (t).
D2 (t)
(8.78a)
(8.78b)
This means that the input-output behavior of (8.69) can be interpreted as the SDE
d
dx1 (t) = B1 (t, dt
)u(t)dt + B2 (t)dv1 (t)
dy(t) = C(t)x(t)dt + dv2 (t)
(8.79a)
(8.79b)
if the conditions of Proposition 8.1 are satisfied. However, note that the internal variables
x2 (t) may depend directly on the noise process v1 (t). This is questionable if x2 (t) represent physical quantities, so in the following chapters we will discuss how this can be
avoided.
8.7
Difference-Algebraic Equations
In this section, stochastic difference-algebraic equations, or stochastic discrete-time descriptor systems, are discussed.
8.7.1
Noise Modeling
A noise model can be added to a discrete-time descriptor system according to
Ex(t + 1) = Jx(t) + K1 u(t) + K2 v1 (t)
y(t) = Lx(t) + v2 (t),
(8.80a)
(8.80b)
8.7
149
Difference-Algebraic Equations
similarly to the continuous-time case. Here, v1 (t) and v2 (t) are uncorrelated sequences of
white noise and K2 is a constant matrix. We assume that the descriptor system is regular.
In Section 2.6 we saw that discrete-time descriptor systems may be non-causal. The
stochastic system discussed here might be non-causal not only with respect to the input
signal, but also with respect to the noise. This can be seen by first writing the system as
u(t)
Ex(t + 1) = Jx(t) + K1 K2
(8.81a)
v1 (t)
y(t) = Lx(t) + v2 (t)
(8.81b)
and then applying Theorem 2.7. The solutions can be described by
x1 (t + 1) = Ax1 (t) + B1 u(t) + B2 v1 (t)
x2 (t) = −D1 u(t) −
m−1
X
(8.82a)
N i D1 u(t + i)
i=1
− D2 v1 (t) −
m−1
X
N i D2 v1 (t + i)
(8.82b)
i=1
x1 (t)
= Q−1 x(t)
x2 (t)
x (t)
+ v2 (t).
y(t) = LQ 1
x2 (t)
(8.82c)
(8.82d)
A difference from the continuous-time case is that we do not have to put any restriction
on the noise model, as dependence on future values of the noise is theoretically possible.
The dependence on future values of the noise can be handled for example by time shifting
the noise sequence. If we define
ṽ1 (t) = v1 (t + m − 1)
(8.83)
equation (8.82) can be written as
x1 (t + 1) = Ax1 (t) + B1 u(t) + B2 ṽ1 (t − m + 1)
x2 (t) = −D1 u(t) −
m−1
X
(8.84a)
N i D1 u(t + i)
i=1
− D2 ṽ1 (t − m + 1) −
m−1
X
N i D2 ṽ1 (t + i − m + 1)
(8.84b)
i=1
x1 (t)
= Q−1 x(t)
x2 (t)
x1 (t)
y(t) = LQ
+ v2 (t)
x2 (t)
(8.84c)
(8.84d)
which is a causal description with respect to the noise. Note that the sequences v1 (t) and
ṽ1 (t) will have the same statistical properties since they are both white noise sequences.
The noise sequences v1 (t) and v2 (t) must be uncorrelated, otherwise v2 (t) will be correlated with ṽ1 (t − m + 1).
150
8.7.2
8
Linear SDAE Models
Kalman Filtering
The system (8.81) can be transformed into state-space form using the technique in Section 2.6.3. We would then get
z(t + 1) = Ãz(t) + B̃1 u(t + m − 1) + B̃2 v1 (t + m − 1)
y(t) = C̃z(t) + D̃1 u(t + m − 1) + D̃2 v1 (t + m − 1) + v2 (t).
(8.85a)
(8.85b)
which, using (8.83), also can be written as
z(t + 1) = Ãz(t) + B̃1 u(t + m − 1) + B̃2 ṽ1 (t)
y(t) = C̃z(t) + D̃1 u(t + m − 1) + D̃2 ṽ1 (t) + v2 (t).
(8.86a)
(8.86b)
This is a state-space description if u(t + m − 1) is considered as the input. However, it
can be argued that dependence on future noise values is not physical, so another approach
may be to require that N D2 = 0, so that the system is causal with respect to the noise.
We could use a similar approach as in Section 8.2 to make sure that this holds. Note again
that it will not be straightforward to handle the filtering problem for (8.86) if v1 (t) and
v2 (t) are correlated, since this would imply that ṽ1 (t − m + 1) and v2 (t) are correlated.
In this case it is advisable to work with models that are causal with respect to v1 (t).
When the discrete-time descriptor system has been converted into state-space form,
implementation of the Kalman filter is straightforward (e.g., Anderson and Moore, 1979;
Kailath et al., 2000).
Previous work on Kalman filtering of discrete-time descriptor systems is among others Deng and Liu (1999); Nikoukhah et al. (1998, 1999); Darouach et al. (1993); Dai
(1987, 1989a); Chisci and Zappa (1992). The approach taken in this section for discretetime descriptor systems, is similar to the one in Dai (1987). Dai (1987) also uses the idea
to time-shift the noise sequence and write the system in state-space form, but he does not
discuss how a system with input signal should be treated.
8.8
Conclusions
We noted that if noise is added to arbitrary equations of a linear DAE system, derivatives
of the noise signal might affect the internal variables. Since derivatives of white noise
are not well-defined, we derived a method to add noise without causing derivatives of
it to affect the internal variables. Furthermore, if the SDAE system is converted into
state-space form, it is possible to interpret it as an SDE and implement a Kalman filter.
However, it is possible that some internal variables are equal to a white noise process,
and thus have infinite variance. In the following chapters, we will discuss how this can be
avoided.
We also discussed noise modeling for time-varying linear DAEs and discrete-time
descriptor systems.
9
Well-Posedness of Parameter
Estimation Problems
In this chapter we discuss well-posedness of parameter estimation problems for linear
SDAEs, and also how the parameter estimation problems can be solved.
9.1
Introduction
In the previous chapter we discussed how noise models can be added to linear DAE systems in such a way so that the equations can be interpreted as an SDE. However, we also
saw that this could lead to that some of the internal variables of the DAE have infinite
variance since they are equal to a white noise process. This could possibly be accepted if
the variables do not represent physical quantities, but for example sampled outputs must
have finite variance. If a measured output has infinite variance, it may for example be
difficult to formulate a maximum likelihood problem to estimate unknown parameters.
In this chapter we will therefore discuss conditions that make the parameter estimation
problem well-posed and how the parameter estimation problem can be formed. We will
also discuss frequency domain methods for estimation of the parameters.
9.2
Problem Formulation
When modeling a physical system with noise, it is often reasonable that the included
noise processes wl are not white noise, but instead, for example, have a spectrum φ that
is concentrated at low frequencies. The spectrum may also be parameterized so that it
depends on the unknown parameters θ. It is also common that the initial condition is
151
152
9
Well-Posedness of Parameter Estimation Problems
unknown and therefore has to be parameterized. Summing up, this can be written as
E(θ)ẋ(t) = F (θ)x(t) + G(θ)u(t) +
nw
X
Jl (θ)wl (t, θ)
(9.1a)
l=1
x(t0 , θ) = x0 (θ)
(9.1b)
dim x(t) = n
(9.1c)
where θ is a vector of unknown parameters which lies in the domain DM and wl (t, θ) is
a scalar Gaussian second order stationary process with spectrum
φwl (ω, θ).
(9.2)
The spectrum is assumed to be rational in ω with pole excess 2pl . This means that
lim ω 2pl φwl (ω, θ) = Cl (θ)
ω→∞
0 < Cl (θ) < ∞ for θ ∈ DM .
It will be assumed that the input u(t) is known for all t ∈ [t0 , T ] and that it is differentiable
a sufficient number of times. The condition that the input is known for every t typically
means that it is given at a finite number of sampling instants, and its intersample behavior
between these is known, like piecewise constant, piecewise linear, or band-limited. It will
be assumed the system is regular, i.e., that det(sE − F ) is not zero for all s.
An output vector is measured at sampling instants tk ,
y(tk ) = H(θ)x(tk ) + e(tk )
(9.3)
where e(tk ) is a Gaussian random vector with covariance matrix R2 (k, θ), such that e(tk )
and e(ts ) are independent for k 6= s and also independent of all the processes wl . The
case with an output that is measured at discrete time instances is the most likely situation
in system identification applications, so we choose to adopt this view here.
The problem treated in this chapter is to estimate the unknown parameters θ using
u(t) and y(tk ). As mentioned earlier, problems might arise with differentiated noise
or with elements of the internal variables x(t) being equal to white noise (which has
infinite variance). It must therefore be required that the model structure (9.1) is wellposed. The definition of well-posedness that we will use states the minimal requirements
that makes it possible to form a maximum likelihood estimator for the parameters. The
first requirement is that the DAE is regular, since this guarantees a unique solution in the
absence of noise. The second requirement is that the sampled measurements y(tk ) have
finite variance. This means that the equations do not implicitly specify that y(tk ) contains
continuous-time white noise or derivatives of continuous-time white noise.
Definition 9.1 (Well-posedness). Let x(t) be defined as the solution to (9.1) for a θ ∈
DM . The problem to estimate θ from knowledge of u(t), t ∈ [t0 , T ] and y(tk ), k =
1, . . . , N ; tk ∈ [t0 , T ] is well-posed if H(θ)x(tk ) has finite variance and (9.1) is regular
for all θ ∈ DM .
Note that the initial value x0 (θ) may not be chosen freely when computing x(t, θ) (see
Section 9.5). The possibly conflicting values in x0 (θ) will be ignored, and actually have
9.3
153
Main Result
no consequence for the computation of x(t, θ) for t > t0 . For a well-posed estimation
problem the likelihood function which is the value of the joint probability density function
for the random vectors y(tk ) at the actual observations can be computed. Also this will
be discussed in Section 9.5.
9.3
Main Result
The main result of this chapter is the characterization of a well-posed model structure,
which is presented in this section. Before presenting the result, some notation must be
introduced. Let the range and null space of a matrix A be denoted by
V(A) and N (A)
respectively. Furthermore, the following definition of an oblique projection will be used.
Definition 9.2 (Oblique projection). Let B and C be spaces with B ∩ C = {0} that
together span Rn . Let the matrices B̄ and C̄ be bases for B and C respectively. The
oblique projection of a matrix A along B on C is defined as
A/B C , 0
C̄
B̄
C̄
−1
A.
(9.4)
Note that the projection is independent of the choice of bases for B and C. This definition basically follows the definition by van Overschee and De Moor (1996, Section 1.4.2).
However, we here consider projections along column spaces instead of row spaces. Also,
the conditions on the spaces B and C give a simpler definition. The more general version
by van Overschee and De Moor (1996) is not necessary here. The main result can now be
formulated as follows.
Theorem 9.1
Consider the model (9.1). Let λ(θ) be a scalar such that λ(θ)E(θ) + F (θ) is invertible.
Let
−1
Ē(θ) = λ(θ)E(θ) + F (θ)
E(θ).
(9.5)
Assuming the model (9.1) is regular, the estimation problem (9.1)–(9.3) is well-posed if
and only if
h
i.
−1
N Ē n (θ) ∈ N H(θ) j ≥ pl , ∀l.
Ē j (θ) λ(θ)E(θ) + F (θ)
Jl (θ)
V Ē n (θ)
(9.6)
Proof: See Appendix B.
Note that any λ(θ) can be used to check if an estimation problem is well-posed, as
long as λ(θ)E(θ) + F (θ) is invertible. This follows directly from the theorem, since (9.6)
is equivalent to well-posedness for every λ(θ) with invertible λ(θ)E(θ) + F (θ).
154
9
9.4
Measuring Signals with Infinite Variance
Well-Posedness of Parameter Estimation Problems
It may happen that a selected output has infinite instantaneous variance. This happens
when condition (9.6) is violated. This is best illustrated by an example: Let the SDAE be
ẋ1 (t) = −2x1 (t) + v(t)
0 = −x2 (t) + v(t)
(9.7a)
(9.7b)
where v(t) is continuous-time white noise. We would like to measure x1 + x2 . This
is not a well-posed problem since x2 has infinite variance. A convenient way of dealing
with this in a modeling situation, would be to explicitly introduce a presampling, low pass
filter, to make the measured variable
x3 (t) =
1
x1 (t) + x2 (t) .
0.01p + 1
Including this new variable in the SDAE gives
ẋ1 (t) = −2x1 (t) + v(t)
ẋ3 (t) = −100x3 (t) + 100x1 (t) + 100v(t)
0 = −x2 (t) + v(t)
with the sampled measurements
y(tk ) = x3 (tk ) + e(tk ).
This is a well-posed problem. The method suggested here is related to the sampling
method described in Lemma 2.5.
9.5
The Log-Likelihood Function and the Maximum
Likelihood Method
To implement the maximum likelihood method for parameter estimation, it is necessary
to compute the likelihood function. The likelihood function for the estimation problem is
computed from the joint probability density function of the observations y(tk ). It is customary to determine this from the conditional densities p[y(tk )|y(t0 ) . . . y(tk−1 ), u(·), θ].
See, e.g., Ljung (1999, Section 7.4). In other words, we need the one-step-ahead predictions of the measured outputs.
By representing the disturbances wl (t, θ) as outputs from linear filters driven by white
noise vl (t) (which is possible, since they have rational spectral densities), the SDAE can
be transformed into state-space form using the techniques discussed in Section 2.3. This
is done by first representing the noise processes wl (t, θ) as
ẋw (t) = Aw (θ)xw (t) + Bw (θ)v(t)
(9.8a)
w(t, θ) = Cw (θ)xw (t) + Dw (θ)v(t)
(9.8b)
9.5
155
The Log-Likelihood Function and the Maximum Likelihood Method
where

v1 (t)


v(t) =  ... 

(9.9)
vnv (t)
is white noise with covariance R1 (θ)δ(t) and

w1 (t, θ)


..
w(t, θ) = 
.
.
wnw (t, θ)

(9.10)
As discussed in Section 2.7.2, this should be interpreted as a stochastic integral. By
writing
J(θ) = J1 (θ) · · · Jnw (θ)
(9.11)
(9.1), (9.3), and (9.8) can be combined to give
E(θ) 0
0
I
ẋ(t)
F (θ) J(θ)Cw (θ)
x(t)
=
ẋw (t)
0
Aw (θ)
xw (t)
J(θ)Dw (θ)
G(θ)
u(t) +
v(t)
+
Bw (θ)
0
x(tk )
y(tk ) = H(θ) 0
+ e(tk ).
xw (tk )
(9.12a)
(9.12b)
Under the assumption of regularity, this DAE can, using Theorem 2.3, be transformed
into the form
(9.13a)
ẋ1 (t) = A(θ)x1 (t) + G1 (θ)u(t) + J1 (θ)v(t)
m−1
d
d
x2 (t) = − I + N (θ) + · · · + m−1 N m−1 (θ) G2 (θ)u(t) + J2 (θ)v(t)
dt
dt
(9.13b)
y(tk ) = C1 (θ)x1 (tk ) + C2 (θ)x2 (tk ) + e(tk ).
(9.13c)
Inserting (9.13b) into (9.13c) gives (omitting dependence on θ)
m l−1
X
d
l−1
N
Ga u(tk ) + Ja v(tk ) + e(tk ).
y(tk ) = C1 x1 (tk ) − C2
dtl−1
l=1
If it is assumed that the SDAE forms a well-posed estimation problem, y(tk ) does not
depend on time-continuous white noise, i.e., v(t). This means that y(tk ) can be written
as
m l−1
X
d
l−1
N (θ)G2 (θ)u(tk ) + e(tk ).
y(tk ) = C1 (θ)x1 (tk ) − C2 (θ)
dtl−1
l=1
156
9
Well-Posedness of Parameter Estimation Problems
Summing up, the original linear SDAE can be transformed into the form
ẋ1 (t) = A(θ)x1 (t) + G1 (θ)u(t) + J1 (θ)v(t)
(9.14a)
m l−1
X
d
y(tk ) = C1 (θ)x1 (tk ) − C2 (θ)
N l−1 (θ)G2 (θ)u(tk ) + e(tk )
dtl−1
l=1
(9.14b)
v(t) = v1 (t) v2 (t) · · ·
T
vnv (t)
T
Ev(t)v (s) = R1 (θ)δ(t − s)
(9.14c)
(9.14d)
T
Ee(tk )e (ts ) = R2 (k, θ)δtk ,ts .
(9.14e)
This is a standard linear prediction problem with continuous-time dynamics, continuoustime white noise, and discrete-time measurements. The Kalman filter equations for this
are given, e.g., by Jazwinski (1970), and they define the one-step-ahead predicted outputs
ŷ(tk |tk−1 , θ) and the prediction error variances Λ(tk , θ). With Gaussian disturbances, we
obtain in the usual way the log-likelihood function
VN (θ) =
N
T
1X
y(tk ) − ŷ(tk |tk−1 , θ) Λ−1 (tk , θ)
2
k=1
× y(tk ) − ŷ(tk |tk−1 , θ) + log det Λ(tk , θ). (9.15)
The parameter estimates are then computed as
θ̂M L = arg min VN (θ).
(9.16)
θ
If a general norm of the prediction errors,
ε(tk , θ) = y(tk ) − ŷ(tk |tk−1 , θ),
(9.17)
is minimized, we get the prediction error method.
In practice, the important question of how the state-space description should be computed remains. As discussed in Chapter 11, the form (9.14) can be computed using numerical software. But if some elements of the matrices are unknown, numerical software
cannot be used. Another approach could be to calculate the canonical forms using symbolical software. But this approach has not been thoroughly investigated, and symbolical
software is usually not as easily available as numerical software. The remedy is to make
the conversion using numerical software for each value of the parameters that the identification algorithm needs. Consider for example the case when the parameters are to be
estimated by minimizing (9.15) using a Gauss-Newton search. For each parameter value θ
that the Gauss-Newton algorithm needs, the transformed system (9.14) can be computed.
If the initial condition of the system is unknown, it should be estimated along with
the parameters. For state-space systems, this is done by parameterizing the initial state,
x(t0 ) = x0 (θ). For linear SDAE systems care must be taken when parameterizing the
initial value. From (B.3) on page 197 of Appendix B we get that
xs (t0 )
T
(θ)
T
(θ)
x(t0 ) = 1
.
(9.18)
2
xa (t0 )
9.6
157
Frequency Domain Identification
It is also obvious from the transformed system equations (B.4a) and (B.8) that xs (t0 ) can
be parameterized freely, while xa (t0 ) is specified by the input and noise signals. The part
of x(t0 ) that can be parameterized is thus
V Ē n (θ)
xs (t0 ) = x(t0 )/V(T2 ) V(T1 ) = x(t0 )/
(9.19)
n
N Ē (θ)
where Ē(θ) is the matrix defined in (9.5). Note that since xa is determined by (B.8), any
initial conditions that are specified for xa can be ignored in the identification procedure
since they do not affect the likelihood function.
9.6
Frequency Domain Identification
The work which has been done this far has been based on transforming the DAE system
into a state-space-like system, and using identification methods for state-space descriptions. As was discussed earlier, this transformation always exists if the system is regular,
and can be computed numerically. However, we have seen that the work to transform
a linear DAE system into state-space form might be significant in some cases. Furthermore, the output can depend on derivatives of the input. If the input can be selected, then
it might be possible to differentiate it analytically. If, on the other hand, only a measured
input is available, it must be differentiated numerically, which can be a problem if the
signal is noisy.
Here, we examine another approach to the identification problem that offers an alternative way to handle these potential problems, namely identification in the frequency
domain. The conversion into state-space form can be avoided in the output error case as
we will see below. A model which differentiates the input will have a large amplification
for high frequencies. In the frequency domain we could therefore handle this problem
by not including measurements with a too high frequency in Z N = {U (ω1 ), Y (ω1 ), . . . ,
U (ωN ), Y (ωN )}.
As discussed in Section 3.3, it is assumed that the model structure is specified by
transfer functions (or matrices of transfer functions) according to
y(t) = G(p, θ)u(t) + H(p, θ)e(t)
(9.20)
when performing frequency domain identification. H(p, θ) is assumed to have a causal
inverse.
A linear DAE system with only measurement noise (an output error model),
E(θ)ẋ(t) = J(θ)x(t) + K1 (θ)u(t)
(9.21a)
y(t) = L(θ)x(t) + e(t),
(9.21b)
can be transformed directly into the form (9.20) under the usual assumption of regularity.
The only difference from the transfer function of a state-space system is that G(p, θ) may
be non-proper here. The transfer functions are
−1
G(p, θ) = L(θ) (pE(θ) − J(θ))
H(p, θ) = 1.
K1 (θ)
(9.22a)
(9.22b)
158
9
Well-Posedness of Parameter Estimation Problems
When the transfer function has been calculated, all we have to do is to plug it into
any identification algorithm for the frequency domain. Books which treat this are e.g.,
Ljung (1999) and Pintelon and Schoukens (2001). Note that G(p, θ) here easily could
be calculated using symbolical software. We can therefore compute G(p, θ) once and for
all, and do not have to perform the calculation for each parameter value. One possible
selection of identification method is to minimize the criterion
VN (θ, Z N ) =
N
X
2
kY (ωk ) − G(iωk , θ)U (ωk )k
(9.23)
k=1
with respect to the parameters θ.
Estimates of the Fourier transforms of the input and output signals are needed. As
discussed in Section 3.3, these could be provided directly by a special measurement device
or estimated from time domain data. A drawback with identification in the frequency
domain is that knowledge of the initial values of the internal variables is more difficult to
utilize than for time domain identification.
In the more complex case when the model also has process noise,
E(θ)ẋ(t) = J(θ)x(t) + K1 (θ)u(t) + K2 (θ)v1 (t)
y(t) = L(θ)x(t) + v2 (t),
(9.24a)
(9.24b)
the noise filter H(p, θ) cannot be calculated in a straightforward manner. One approach to
calculate H(p, θ) here is to first transform the DAE system into state-space form and then
compute the Kalman filter. We now in principle need to do the same transformation that
needs to be done when estimating the parameters in the time domain. We therefore do not
have the possibility of calculating H(p, θ) once and for all with symbolical software as
could be done for the output error case.
9.7
Time-Varying Linear SDAE Models
In this section we will examine well-posedness of the problem to estimate unknown parameters θ in the time-varying linear DAE
E(t, θ)ẋ(t) = F (t, θ)x(t) + f (t, θ)
x(t0 , θ) = x0 (θ)
dim x(t) = n.
where
f (t, θ) = G(t, θ)u(t) +
(9.25a)
(9.25b)
(9.25c)
nw
X
Jl (t, θ)wl (t, θ).
(9.26)
l=1
Measurements are collected at time instances tk ,
y(tk ) = H(tk , θ)x(tk ) + e(tk ).
(9.27)
As before, wl (t, θ) is a Gaussian second order stationary process with spectrum
φwl (ω, θ)
(9.28)
9.7
159
Time-Varying Linear SDAE Models
where the spectrum is assumed to be rational in ω with pole excess 2pl . We will use the
definition of well-posedness from the time-invariant case modified for the time varying
case.
Definition 9.3 (Well-posedness). Let x(t) be defined as the solution to (9.25a) for a
θ ∈ DM . The problem to estimate θ from knowledge of u(t), t ∈ [t0 , T ], and y(tk ), k =
1, . . . , N ; tk ∈ [t0 , T ] is well-posed if H(tk , θ)x(tk ) has finite variance and (9.25a) is
regular for all θ ∈ DM .
Here, regularity means that no part of x is undetermined as discussed in Section 2.4.
To examine well-posedness of (9.25), we examine (9.25a) transformed according to Theorem 2.6.
ẋ1 (t) = A13 (t, θ)x3 (t) + f1 (t, θ)
(9.29a)
0 = x2 (t) + f2 (t, θ)
(9.29b)
0 = f3 (t, θ)
(9.29c)
where


f1 (t, θ)
f2 (t, θ) = Pm+1 (t, θ)P̃m (t, θ, d ) · · · P̃1 (t, θ, d ) ×
dt
dt
f3 (t, θ)
G(t, θ)u(t) +
nw
X
!
Jl (t, θ)wl (t, θ) . (9.30)
l=1
The system is assumed to be regular, so x3 and f3 are of size zero. We want to examine if
x1 (tk )
H(tk , θ)x(tk ) = H(tk , θ)Q(tk , θ)
(9.31)
x2 (tk )
has finite variance (x3 is removed since it is of size zero). Theorem 2.6 gives that f1 does
not depend on derivatives of f , so x1 is always well-defined with finite variance through
the SDE
ẋ1 (t) = f1 (t, θ).
(9.32)
We must thus examine if
0
0
H(tk , θ)Q(tk , θ)
= H(tk , θ)Q(tk , θ)
x2 (tk )
−f2 (tk )
(9.33)
has finite variance. For this expression to have finite variance, it must be guaranteed
that it does not depend on too high derivatives of wl , l = 1, . . . , nw . Each wl can be
differentiated at most pl − 1 times since its spectrum has pole excess 2pl . This can be
dn
realized from (2.179b) which gives that the variance of dt
n wl is
Z∞
r(0) =
−∞
(
<∞
(iw) φwl dω
=∞
2n
if n ≤ pl − 1
if n ≥ pl .
(9.34)
160
9
Well-Posedness of Parameter Estimation Problems
Further transforming (9.33) we get
0
0 0
f1 (tk , θ)
H(tk , θ)Q(tk , θ)
= H(tk , θ)Q(tk , θ)
=
x2 (tk )
0 I
f2 (tk , θ)
0 0
d
d
= H(t, θ)Q(t, θ)
Pm+1 (t, θ)P̃m (t, θ, dt
) · · · P̃1 (t, θ, dt
)×
0 I
!
nw
X
G(t, θ)u(t) +
Jl (t, θ)wl (t, θ) . (9.35)
l=1
t=tk
Note that the derivative should be applied before inserting t = tk . The expression shows
that we must require that the expression
0 0
d
d
) · · · P̃1 (t, θ, dt
)Jl (t, θ)wl (t, θ)
H(t, θ)Q(t, θ)
Pm+1 (t, θ)P̃m (t, θ, dt
0 I
t=tk
(9.36)
for l = 1, . . . , nw does not contain higher derivatives than pl − 1 of wl . We formalize this
result with a proposition.
Proposition 9.1
The estimation problem (9.25)–(9.28) is well-posed if and only if (9.25a) is regular and
0 0
d
d
Pm+1 (t, θ)P̃m (t, θ, dt ) · · · P̃1 (t, θ, dt )Jl (t, θ)
H(t, θ)Q(t, θ)
(9.37)
0 I
t=t
k
d
is of at most order pl − 1 in dt
for l = 1, . . . , nw , for all tk , and θ ∈ DM . The matrices
P and Q are the transformation matrices defined in Section 2.4.
Note that the derivatives should be handled as operators when applied to Jl , for example
d
d
t=1+t .
(9.38)
dt
dt
When an estimation problem is well-posed, (9.32) and (9.27) can be used to compute
the likelihood function for the output.
9.8
Difference-Algebraic Equations
In this section, stochastic difference-algebraic equations, or stochastic discrete-time descriptor systems, are discussed.
9.8.1
Time Domain Identification
As discussed in Section 2.6.3 and 8.7, a discrete-time descriptor system can be transformed into a discrete-time state-space system. We can therefore use the prediction error
method or maximum likelihood method as described in Chapter 3. However, as in the
continuous-time case, we are faced with the choice of either calculating the state-space
9.8
161
Difference-Algebraic Equations
description symbolically, or doing it numerically. The approach suggested here is to compute it numerically for each parameter value that a state-space description is necessary,
since it is discussed in the previous chapters how this transformation can be performed.
Consider for example the case when we wish to estimate the parameters by minimizing the prediction error criterion
VN (θ, Z N ) =
N
1 X1 T
ε (t, θ)Λ−1 ε(t, θ)
N t=1 2
(9.39)
using a Gauss-Newton search. As in the continuous-time case, we for each parameter
value θ that the Gauss-Newton algorithm needs compute a state-space description using
the methods in Chapter 11 and then calculate the prediction errors ε(t, θ).
In can be noted that the discrete-time parameter estimation problem does not suffer from the same well-posedness issues as the continuous-time case. This is because
discrete-time noise processes always have finite variance.
9.8.2
Frequency Domain Identification
Analogously to the continuous-time case, frequency domain identification is a way to
avoid having to transform the descriptor system into state-space form. For frequency
domain identification in discrete-time, it is assumed that the system is described by
y(t) = G(q, θ)u(t) + H(q, θ)e(t),
(9.40)
as discussed in Section 3.3. H(q, θ) is assumed to have a causal inverse.
A linear discrete-time descriptor system with an output error noise model,
E(θ)x(t + 1) = J(θ)x(t) + K1 (θ)u(t)
y(t) = L(θ)x(t) + e(t),
(9.41a)
(9.41b)
has the transfer functions
−1
G(q, θ) = L(θ) (qE(θ) − J(θ))
K1 (θ)
H(q, θ) = 1.
(9.42a)
(9.42b)
We can here plug G(q, θ) directly into a criterion like
VN (θ, Z N ) =
N
X
Y (ωk ) − G(eiωk , θ)U (ωk )2 .
(9.43)
k=1
As in the continuous-time case, the situation is more complicated if we have a full
noise model as in
E(θ)x(t + 1) = J(θ)x(t) + K1 (θ)u(t) + K2 (θ)v1 (t)
y(t) = L(θ)x(t) + v2 (t).
(9.44a)
(9.44b)
Also here, the simplest way to calculate H(q, θ) is probably to go via a state-space description. Consequently, not much is gained compared to using a time domain method.
162
9.9
9
Well-Posedness of Parameter Estimation Problems
Conclusions
The main result of this chapter is Theorem 9.1, where we provide necessary and sufficient
conditions for a parameter estimation problem, formed from a linear SDAE, to be wellposed. We also discussed how the parameter estimation problem can be formed for wellposed problems, both in the time domain and in the frequency domain. Time-varying
DAEs and the discrete-time case were also briefly treated.
10
Well-Posedness of State Estimation
Problems
In this chapter we discuss well-posedness of state estimation problems for linear SDAEs,
and also how these problems can be solved using the Kalman filter.
10.1
Introduction
In the previous chapter we discussed well-posedness of parameters estimation problems,
and concluded that it must be required that the measured output has finite variance to
allow maximum likelihood estimation of unknown parameters. In this chapter we will
discuss state estimation problems, that is estimation of the internal variables of a linear
SDAE. To allow estimation of the internal variables, it must be required that they have
finite variance. We will first discuss the case when the SDAE has colored noise inputs
and discrete-time measurements similarly to the case examined for parameter estimation
in the previous chapter. We will then examine the case when the input is white noise and
the output is not sampled, similarly to the problem solved by continuous-time Kalman
filters.
For references to previous works on well-posedness of state estimation problems, see
Section 4.2.
10.2
Formulations without Continuous-Time White
Noise
We shall in this section give a formulation of an SDAE filtering problem that only explicitly employs stochastic variables with finite variance, similarly to what was done in
the previous chapter. We shall then investigate if it corresponds to a mathematically well163
164
10
Well-Posedness of State Estimation Problems
posed problem. We will therefore consider an SDAE
E ẋ(t) = F x(t) + Gu(t) +
nw
X
Jl wl (t)
(10.1a)
l=1
x(t0 ) = x0
(10.1b)
dim x(t) = n
(10.1c)
where wl (t) is a Gaussian second order stationary process with spectrum φwl (ω) which
is rational in ω with pole excess 2pl . Recall that this means that
0 < lim ω 2pl φwl (ω) < ∞.
ω→∞
(10.1d)
The input u(t) is known for all t ∈ [t0 , T ]. It will also be assumed that it is differentiable
a sufficient number of times. An output vector is measured at sampling instants tk ,
y(tk ) = Hx(tk ) + e(tk ),
k = 1, . . . , N
(10.1e)
where e(tk ) is a Gaussian random vector with covariance matrix Rk , such that e(tk ) and
e(ts ) are independent for k 6= s and also independent of all the processes wl (t).
It is a feature of the modeling techniques mentioned in the introduction that they often
introduce a number of variables that only play a role in intermediate calculations and
are of no interest in themselves. Therefore we introduce the variable x̄ where all (linear
combinations of) components of x that are of interest are collected,
x̄(t) = M x(t)
(10.2)
for some rectangular matrix M .
The estimation problem considered here is well-posed if both the variables to be estimated x̄ and the measured output y have finite variance. This differs from the formulation
in the previous chapter since we also require the internal variables to have finite variance.
Definition 10.1 (Well-posedness). Let x(t) be defined as the solution to (10.1). The
problem to estimate x̄(t) = M x(t) from y(tk ), k = 1, . . . , N ; tk ∈ [t0 , T ] and u(t),
t ∈ [t0 , T ], is well-posed if Hx(tk ) and M x(t) have finite variances and (10.1) is regular.
We shall find that a well-posed filtering problem can be solved by the regular Kalman
filter.
It can be noted that the initial value x(t0 ) cannot be chosen freely, since part of x is
determined by the deterministic input u. Only
x(t0 )/N (Ē n ) V(Ē n )
(10.3)
can be given an arbitrary value. Any conflicting values of x(t0 ) will be ignored and have
no consequence for the estimation of x(t), t > t0 .
The result on well-posedness of the state estimation problem is similar to the result on
well-posedness of the parameter estimation problem, but it must also be required that the
10.2
165
Formulations without Continuous-Time White Noise
internal variables of interest, x̄, have finite variance. To formulate the result, we recall the
definition of an oblique projection of a matrix A along the space B on the space C,
A/B C , 0 C̄
B̄
C̄
−1
A
(10.4)
where B̄ and C̄ are bases for B and C respectively. We can now formulate the main result
of this section.
Theorem 10.1
Consider (10.1). Let λ be a scalar such that (λE + F ) is invertible. Let
Ē = (λE + F )−1 E.
(10.5)
Then the estimation problem (10.1) is well-posed if and only if
j
M
n
−1
j ≥ pl , ∀l
Ē (λE + F ) Jl V(Ē n ) N (Ē ) ∈ N
H
(10.6)
and (10.1) is regular.
Proof: According to Theorem 9.1, Hx(t) and M x(t) have finite variance if and only if
h
Ē j λE + F
−1 i.
Jl
Ē j λE + F
−1 i.
Jl
N Ē n ∈ N M
j ≥ pl , ∀l
(10.7)
N Ē n ∈ N H
j ≥ pl , ∀l.
(10.8)
V
Ē n
V
Ē n
and
h
This gives (10.6).
Now, consider the problem to estimate x̄(t) using the Kalman filter. First note that
since the disturbances wl (t) have rational spectra, they can be written as outputs from
linear filters driven by white noise v(t),
ẋw (t) = Aw xw (t) + Bw v(t)
(10.9a)
w(t) = Cw xw (t) + Dw v(t)
(10.9b)
where


w1 (t)


w(t) =  ... 
wnw (t)
(10.10)
and v(t) is white noise with variance R1 δ(t). This should be interpreted as an SDE, see
Section 2.7.2. With
J = J1 · · · Jnw ,
(10.11)
166
10
Well-Posedness of State Estimation Problems
(10.1) and (10.9) can be combined to give
E 0
ẋ(t)
F JCw
x(t)
G
JDw
=
+
u(t) +
v(t) (10.12a)
0 I
ẋw (t)
0 Aw
xw (t)
0
Bw
x(t)
(10.12b)
x̄(t) = M x(t) = M 0
xw (t)
x(tk )
+ e(tk ).
(10.12c)
y(tk ) = Hx(tk ) + e(tk ) = H 0
xw (tk )
Assuming that the SDAE is regular, Theorem 2.3 can be used to transform this description
into the form
x1 (t)
x(t)
= Q1 Q2
(10.13a)
x2 (t)
xw (t)
M
H
ẋ1 (t) = Ax1 (t) + G1 u(t) + J1 v(t)
dm−1 m−1
0 Q2 x2 (t) = − M 0 Q2 I + · · · + m−1 N
G2 u(t)
dt
dm−1
0 Q2 x2 (t) = − H 0 Q2 I + · · · + m−1 N m−1 G2 u(t)
dt
(10.13b)
(10.13c)
(10.13d)
provided that the estimation problem is well-posed so that Hx(t) and M x(t) do not contain white noise components. Together with the measurement equation
x1 (tk )
+ e(tk )
(10.14)
y(tk ) = Hx(tk ) + e(tk ) = H 0 Q1 Q2
x2 (tk )
this finally gives the state-space description
ẋ1 (t) = Ax1 (t) + G1 u(t) + J1 v(t)
(10.15a)
dm−1 m−1
y(tk ) = H 0 Q1 x1 (tk ) − H 0 Q2 I + · · · + m−1 N
G2 u(tk ) + e(tk ).
dt
(10.15b)
This state-space description gives a filtering problem with continuous-time dynamics and
discrete-time measurements. The Kalman filter for this setting provided, e.g., by Jazwinski (1970), can be used to estimate x1 . The estimate of x̄ is then computed from the
estimate of x1 and the deterministic input using
x1 (t)
x̄(t) = M 0 Q1 Q2
(10.16)
x2 (t)
and (10.13c).
10.3
Formulations with Continuous-Time White Noise
For stochastic state-space systems, the case with a white noise input and continuous-time
measurements is often considered. We will therefore consider this problem also for DAE
10.3
Formulations with Continuous-Time White Noise
167
systems. We will thus examine the SDAE
E ẋ(t) = F x(t) + Gu(t) + Jv(t)
y(t) = Hx(t) + e(t)
x(t0 ) = x0
dim x(t) = n
(10.17a)
(10.17b)
(10.17c)
(10.17d)
where the stochastic processes v and e are continuous-time white noise. It is assumed that
the system is regular. Also here we collect the (linear combination) of variables that are
of interest in a vector x̄,
x̄ = M x.
(10.18)
To be able to estimate the variables x̄, we must as before require that they have finite
variance. However, continuous-time Kalman filtering theory allows that the output y
contains white noise signals, but not any derivatives of white noise (which would not be
well-defined). In this case we therefore define well-posedness as follows.
Definition 10.2 (Well-posedness). Let x(t) be defined as the solution to (10.17). The
problem to estimate x̄(t) = M x(t) from y(t) and u(t), t ∈ [t0 , T ], is well-posed if
M x(t) has finite variance, Hx(t) does not contain derivatives of white noise, and (10.17)
is regular.
We shall find that a well-posed estimation problem with white noise inputs can be
solved using a Kalman filter. As discussed previously, the initial value x0 may not be
chosen freely. The possibly conflicting values in x0 will be ignored, and actually have no
consequence for the computation of x(t) for t > t0 .
Well-posedness is characterized by the following theorem.
Theorem 10.2
Consider (10.17). Let λ be a scalar such that (λE + F ) is invertible. Let
Ē = (λE + F )−1 E.
Then the estimation problem (10.17) is well-posed if and only if
j
Ē (λE + F )−1 J V(Ē n ) N (Ē n ) ∈ N (M ), j ≥ 0
j
Ē (λE + F )−1 J V(Ē n ) N (Ē n ) ∈ N (H), j ≥ 1.
(10.19)
(10.20a)
(10.20b)
are satisfied and (10.17) is regular.
Proof: (10.20a) follows directly from Theorem 9.1 since white noise has pole excess
pl = 0. To derive (10.20b), we examine (B.8) in the proof of Theorem 9.1 in Appendix B,
d
m−1
d
N m−1
+λ N +· · ·+
+λ
Ga u(t)+Ja w(t) . (10.21)
xa (t) = − I +
dt
dt
Note that all J-matrices can be grouped together since all noise signals have the same
pole excess. Since
Hx(t) = HT1 xs (t) + HT2 xa (t)
(10.22)
168
10
Well-Posedness of State Estimation Problems
(with notation from Appendix B) it must be required that
HT2 N j Ja = 0,
j≥1
(10.23)
to avoid derivatives of white noise. Now, (10.23) can be rewritten as
0 = HT2 N j Ja
−1 = H 0 T2 T1 T2
T1 Esj Js + T2 N j Ja
= H T1 Esj Js + T2 N j Ja V(T ) V(T2 )
1
Esj
Js
0
V(T2 )
= H T1 T2
Ja
0 Nj
V(T1 )
= H Ē j (λE + F )−1 J V(T ) V(T2 )
1
(10.24)
which gives (10.20b) since V(T2 (θ)) = N (Ē n (θ)) and V(T1 (θ)) = V(Ē n (θ)).
To see how a Kalman filter can be formulated, we rewrite (10.17) using Theorem 2.3.
Under the assumption of well-posedness, this takes the form
x1 (t)
x(t) = Q1 Q2
(10.25a)
x2 (t)
ẋ1 (t) = Ax1 (t) + G1 u(t) + J1 v(t)
dm−1 m−1
G2 u(t)
M Q2 x2 (t) = −M Q2 I + · · · + m−1 N
dt
dm−1
HQ2 x2 (t) = −HQ2 I + · · · + m−1 N m−1 G2 u(t) − HQ2 J2 v(t)
dt
x1 (t)
y(t) = Hx(t) + e(t) = H Q1 Q2
+ e(t).
x2 (t)
(10.25b)
(10.25c)
(10.25d)
(10.25e)
Inserting (10.25d) into (10.25e) gives the state-space description
ẋ1 (t) = Ax1 (t) + G1 u(t) + J1 v(t)
(10.26a)
m−1
d
y(t) = HQ1 x1 (t) − HQ2 I + · · · + m−1 N m−1 G2 u(t) − HQ2 J2 v(t) + e(t).
dt
(10.26b)
This state-space description gives a continuous-time filtering problem with correlated process and measurement noise. The Kalman filter for this problem which is given by, e.g.,
Kailath et al. (2000), can be used to estimate x1 . The estimate of x̄ is then computed from
the estimate of x1 and the deterministic input using
x1 (t)
x̄(t) = M 0 Q1 Q2
(10.27)
x2 (t)
and (10.25c).
10.4
169
Example
10.4
Example
This section presents an example that demonstrates the principles of the results discussed
in the chapter. Consider two bodies, each with unit mass, moving in one dimension with
velocities v1 and v2 and subject to external forces w1 and w2 respectively. If the two
bodies are joined together the situation is described by the following set of equations
v̇1 (t) = f (t) + w1 (t)
v̇2 (t) = −f (t) + w2 (t)
(10.28)
0 = v1 (t) − v2 (t)
where f is the force acting between the bodies. It is typical of the models obtained when
joining components from model libraries that too many variables are included. (In this
simple case it is of course obvious to the human modeler that this model can be simplified
to that of a body with mass 2 accelerated by w1 + w2 .) In the notation of (10.1) we have,
with
 
v1
x = v2  ,
f




 
 
1 0 0
0 0
1
1
0
E = 0 1 0 F = 0 0 −1 G = 0 J1 = 0 J2 = 1 .
0 0 0
1 −1 0
0
0
With λ = 1 we get

1
1
1
Ē =
2
1
1
1
−1

0
0
0
which gives
 
 1 
R(Ē 3 ) = span 1


0
   
0 
 1
N (Ē 3 ) = span −1 , 0 .


1
0
Using the condition (10.6) we get that
( 0
j
Ē (λE + F )−1 J1 R(Ē 3 ) N (Ē 3 ) =
j
Ē (λE + F )−1 J2 R(Ē 3 ) N (Ē 3 ) =
1
2
0
( 1
2
0
j=0
0
1
j > 0.
0
0
−1
j=0
j > 0.
If w1 and w2 are white noise, the conditions of 10.2 are satisfied as soon as the last column
of M is zero, showing that all linear combinations of v1 and v2 are well-defined with finite
variance. If both w1 and w2 have pole excess greater than zero, all H and M satisfy the
conditions of Theorem 10.1.
170
10.5
10
Well-Posedness of State Estimation Problems
Time-Varying Linear SDAE Models
In this section we will study well-posedness of state estimation problems for time-varying
linear SDAE systems. First consider a system with a white noise input v(t) and white
measurement noise e(t),
E(t)ẋ(t) = F (t)x(t) + G(t)u(t) + J(t)v(t)
(10.29a)
y(t) = H(t)x(t) + e(t)
dim x(t) = n.
(10.29b)
(10.29c)
We will examine when it is possible to compute an estimate of a linear combination
x̄(t) = M (t)x(t) of the internal variables. To do this it is useful to examine the system transformed into the form described by Theorem 2.6,
ẋ1 (t) = A13 (t, θ)x3 (t) + f1 (t, θ)
(10.30a)
0 = x2 (t) + f2 (t, θ)
(10.30b)
0 = f3 (t, θ)
(10.30c)
where


f1 (t, θ)
f2 (t, θ) = Pm+1 (t, θ)P̃m (t, θ, d ) · · · P̃1 (t, θ, d ) G(t)u(t) + J(t)v(t) . (10.31)
dt
dt
f3 (t, θ)
We will assume that the system is regular, so x3 (t) and f3 (t) are of size zero. Theorem 2.6
gives that ẋ1 (t) is not affected by derivatives of u(t) and v(t) so it has finite variance and
is defined by the SDE
ẋ1 (t) = f1 (t, θ).
(10.32)
We also have that
y(t) = H(t)x(t) + e(t) = H(t)Q(t)
x1 (t)
+ e(t).
x2 (t)
(10.33)
(10.32) and (10.33) can be used to compute a filter estimate of x1 (t) using Kalman filtering techniques provided that y(t) does not depend on derivatives of v(t). To avoid this,
we must make sure that
0
H(t)Q(t)
=
−f2 (t)
0 0
d
d
) · · · P̃1 (t, θ, dt
) G(t)u(t) + J(t)v(t)
H(t)Q(t)
Pm+1 (t, θ)P̃m (t, θ, dt
0 I
(10.34)
does not differentiate v(t), or equivalently that
0 0
d
d
H(t)Q(t)
Pm+1 (t, θ)P̃m (t, θ, dt
) · · · P̃1 (t, θ, dt
)J(t)
0 I
(10.35)
10.5
171
Time-Varying Linear SDAE Models
d
is of order zero in dt
. Note that the derivatives should be handled as operators when
applied to J, for example
d
d
t=1+t .
(10.36)
dt
dt
An estimate of x̄(t) can then be computed from
x (t)
x (t)
x̄(t) = M (t)x(t) = M (t)Q(t) 1
= M (t)Q(t) 1
+
x2 (t)
0
0 0
d
d
M (t)Q(t)
) · · · P̃1 (t, θ, dt
) G(t)u(t) + J(t)v(t)
Pm+1 (t, θ)P̃m (t, θ, dt
0 I
(10.37)
if no white noise terms v(t) occur in the expression so it only is a function of the estimated
x1 (t) and the known input u(t). To avoid white noise terms from occurring, it must be
required that
0 0
d
d
) · · · P̃1 (t, θ, dt
)J(t)
(10.38)
Pm+1 (t, θ)P̃m (t, θ, dt
M (t)Q(t)
0 I
is zero, where the derivatives as before should be handled as operators. This discussion
leads to the following proposition.
Proposition 10.1
Consider the regular time-varying linear DAE model (10.29) with v(t) and e(t) considered as white noises. A filter estimate of x̄(t) = M (t)x(t) can be computed using
standard Kalman filtering techniques provided that
0 0
d
d
H(t)Q(t)
Pm+1 (t, θ)P̃m (t, θ, dt
) · · · P̃1 (t, θ, dt
)J(t)
(10.39)
0 I
is of order zero in
d
dt
and
0 0
d
d
M (t)Q(t)
) · · · P̃1 (t, θ, dt
)J(t)
Pm+1 (t, θ)P̃m (t, θ, dt
0 I
(10.40)
is zero, where the derivatives should be handled as operators.
As in the time-invariant case, we will also study the, perhaps more realistic, case with
colored noise and sampled measurements. In this case the DAE can be written as
E(t)ẋ(t) = F (t)x(t) + G(t)u(t) +
nw
X
Jl (t)wl (t)
(10.41a)
l=1
x(t0 ) = x0
dim x(t) = n
(10.41b)
(10.41c)
where wl (t) is a Gaussian second order stationary process with spectrum φwl (ω) which
is rational in ω with pole excess 2pl . An output vector is measured at sampling instants
tk ,
(10.41d)
y(tk ) = H(tk )x(tk ) + e(tk ), k = 1, . . . , N
172
10
Well-Posedness of State Estimation Problems
where e(tk ) is a Gaussian random vector. To be able to compute an estimate of x̄(t) =
M (t)x(t) in this case, it must be guaranteed that wl is differentiated at most pl − 1 times
since it has pole excess 2pl . This holds for both x and y since none of them can include
time-continuous white noise since we have discrete-time measurements. With similar
calculations as was made above, we get the following proposition.
Proposition 10.2
Consider the regular linear time-varying DAE model (10.41). A filter estimate of x̄(t) =
M (t)x(t) can be computed using standard Kalman filtering techniques provided that
0 0
d
d
H(t)Q(t)
Pm+1 (t, θ)P̃m (t, θ, dt ) · · · P̃1 (t, θ, dt )Jl (t)
(10.42)
0 I
t=t
k
and
0
M (t)Q(t)
0
0
d
d
) · · · P̃1 (t, θ, dt
)Jl (t)
Pm+1 (t, θ)P̃m (t, θ, dt
I
(10.43)
d
are at most of order pl − 1, l = 1, . . . , nw in dt
, where the derivatives should be handled
as operators. The derivative should be applied before inserting t = tk .
10.6
Conclusions
We have discussed well-posedness of state estimation problems for linear SDAE systems.
The main results are Theorem 10.1 and 10.2 where the cases without and with continuoustime white noise were treated. The discussion also included methods to solve the state
estimation problem using the Kalman filter. We have also discussed well-posedness of
state estimation problems for time-varying linear SDAEs.
11
Implementation Issues
In this chapter we discuss how the canonical forms for linear DAEs can be computed
using numerical software.
11.1
Introduction
The transformations presented in Section 2.3 have been used extensively in the thesis.
Their existence were proven in Section 2.3, but it was not discussed how they could
actually be computed. To be able use the transformations in a numerical implementation
of an identification or estimation algorithm, it is of course crucial to be able to compute
them numerically in a reliable manner. We will here discuss how this computation can be
performed.
The discussion will include pointers to implementations of some algorithms in the
linear algebra package LAPACK (Anderson et al., 1999). LAPACK is a is a free collection
of routines written in Fortran77 that can be used for systems of linear equations, leastsquares solutions of linear systems of equations, eigenvalue problems, and singular value
problems. LAPACK is more or less the standard way to solve this kind of problems,
and is used in commercial software like M ATLAB. For operations that can be easily
implemented in for example M ATLAB or Mathematica, such as matrix multiplication and
inversion, no pointers to special implementations will be made.
Some ideas related to the method presented in this section for computing the canonical
forms, have earlier been published by Varga (1992). However, the presentation here is
more detailed, and is closely connected to the derivation of the canonical forms presented
in Section 2.3. Furthermore, we will use software from LAPACK.
In Section 11.2 we will discuss generalized eigenvalue problems and some tools which
are used for solving these problems, as these are the tools which we will use to compute
the canonical forms. In Section 11.3 we then discuss how the actual computation is per173
174
11
Implementation Issues
formed. The chapter is concluded with an example, a summary of the algorithm for computing the canonical forms and a note on how the results can be used in the discrete-time
case.
11.2
Generalized Eigenvalues
The computation of the canonical forms will be performed with tools that normally are
used for computation of generalized eigenvalues. Therefore, some theory for generalized
eigenvalues will be presented in this section. The theory presented here can be found in
for example the books by Bai et al. (2000) and Golub and van Loan (1996, Section 7.7).
Consider a matrix pencil
λE − J
(11.1)
where the matrices E and J are n × n with constant real elements and λ is a scalar
variable. We will assume that the pencil is regular, that is
det(λE − J) 6≡ 0
(11.2)
with respect to λ. The generalized eigenvalues are defined as those λ for which
det(λE − J) = 0.
(11.3)
If the degree p of the polynomial det(λE − J) is less than n, the pencil also has n − p
infinite generalized eigenvalues. This happens when rank E < n (Golub and van Loan,
1996, Section 7.7). We illustrate the concepts with an example.
Example 11.1: Generalized eigenvalues
Consider the matrix pencil
λ
We have that
1
det λ
0
1
0
−1
0
−
1
0
0
−1
−
0
1
0
.
−1
(11.4)
0
−1
=1+λ
(11.5)
so the matrix pencil has two generalized eigenvalues, ∞ and −1.
Generalized eigenvectors will not be discussed here, the interested reader is instead referred to for example Bai et al. (2000).
Since it may be difficult to solve (11.3) for the generalized eigenvalues, different transformations of the matrices that simplifies computation of the generalized eigenvalues exist. The transformations are of the form
P (λE − J)Q
(11.6)
with invertible matrices P and Q. Such transformations do not change the eigenvalues
since
det P (λE − J)Q = det(P ) det(λE − J) det(Q).
(11.7)
11.3
Computation of the Canonical Forms
175
One such form is the Kronecker canonical form of a matrix pencil discussed by e.g.,
Gantmacher (1960) and Kailath (1980). However, this form cannot in general be computed numerically in a reliable manner (Bai et al., 2000). For example it may change
discontinuously with the elements of the matrices E and J. The transformation which
we will use here is therefore instead the generalized Schur form which requires fewer
operations and is more stable to compute (Bai et al., 2000).
The generalized Schur form of a real matrix pencil is a transformation
P (λE − J)Q
(11.8)
where P EQ is upper quasi-triangular, that is it is upper triangular with some 2 by 2
blocks corresponding to complex generalized eigenvalues on the diagonal and P JQ is
upper triangular. P and Q are orthogonal matrices. The generalized Schur form can be
computed with the LAPACK commands dgges or sgges. These commands also give
the possibility to sort certain generalized eigenvalues to the lower right. An algorithm for
ordering of the generalized eigenvalues is also discussed by Sima (1996). Here we will
use the possibility to sort the infinite generalized eigenvalues to the lower right.
The generalized Schur form discussed here is also called the generalized real Schur
form, since the original and transformed matrices only contain real elements.
11.3
Computation of the Canonical Forms
The discussion in this section is based on the steps of the proof of the form in Theorem 2.3.
We therefore begin by examining how the diagonalization in Lemma 2.1 can be performed
numerically.
The goal is to find matrices P1 and Q1 such that
J1 J2
E1 E2
(11.9)
−
P1 (λE − J)Q1 = λ
0 J3
0 E3
where E1 is non-singular, E3 is upper triangular with all diagonal elements zero and J3
is non-singular and upper triangular. This is exactly the form we get if we compute the
generalized Schur form with the infinite generalized eigenvalues sorted to the lower right.
This computation can be performed with the LAPACK commands dgges or sgges. In
version 7 and higher of M ATLAB, the functions qz and ordqz can be used. E1 corresponds to finite generalized eigenvalues and is therefore non-singular and E3 corresponds
to infinite generalized eigenvalues and is upper triangular with zero diagonal elements.
J3 is non-singular (and thus upper triangular with non-zero diagonal elements), otherwise
the pencil would not be regular.
The next step is to compute the matrices L and R in Lemma 2.2, that is we want to
solve the system
I L
E1 E2
I R
E1 0
=
(11.10a)
0 I
0 E3
0 I
0 E3
I L
J1 J2
I R
J1 0
=
.
(11.10b)
0 I
0 J3
0 I
0 J3
176
11
Implementation Issues
Performing the matrix multiplication on the left hand side of the equations yields
E1 E1 R + E2 + LE3
E1 0
=
0
E3
0 E3
J1 J1 R + J2 + LJ3
J1 0
=
0
J3
0 J3
(11.11a)
(11.11b)
which is equivalent to the system
E1 R + LE3 = −E2
(11.12a)
J1 R + LJ3 = −J2 .
(11.12b)
Equation (11.12) is a generalized Sylvester equation (Kågström, 1994). The generalized
Sylvester equation (11.12) can be solved from the linear system of equations (Kågström,
1994)
− stack(E2 )
stack(R)
In ⊗ E1 E3T ⊗ Im
=
.
(11.13)
− stack(J2 )
stack(L)
In ⊗ J1 J3T ⊗ Im
Here In is an identity matrix with the same size as E3 and J3 , Im is an identity matrix with
the same size as E1 and J1 , ⊗ represents the Kronecker product and stack(X) denotes
an ordered stack of the columns of a matrix X from left to right starting with the first
column.
The system (11.13) can be quite large, so it may be a better choice to solve the generalized Sylvester equation (11.12) using specialized software such as the LAPACK routines
stgsyl or dtgsyl.
The steps in the proof of Lemma 2.3 and Theorem 2.3 only contain standard matrix
manipulations, such as multiplication and inversion. They are straightforward to implement, and will not be discussed further here.
11.4
Summary
In this section a summary of the steps to compute the canonical forms is provided. It
can be used to implement the computations without studying Section 11.3 in detail. The
summary is provided as a numbered list with the necessary computations.
1. Start with a system
E ẋ(t) = Jx(t) + Ku(t)
y(t) = Lx(t)
that should be transformed into
I 0
A
−1
Q ẋ(t) =
0 N
0
0
B
−1
Q x(t) +
u(t)
I
D
(11.14a)
(11.14b)
(11.15)
11.4
177
Summary
or
ẋ1 (t) = Ax1 (t) + Bu(t)
x2 (t) = −Du(t) −
m−1
X
N i Du(i) (t)
(11.16a)
(11.16b)
i=1
x1 (t)
= Q−1 x(t)
x2 (t)
x1 (t)
y(t) = LQ
.
x2 (t)
(11.16c)
(11.16d)
2. Compute the generalized Schur form of the matrix pencil λE − J so that
J1 J2
E1 E2
.
(11.17)
−
P1 (λE − J)Q1 = λ
0 J3
0 E3
The generalized eigenvalues should be sorted so that diagonal elements of E1 are
non-zero and the diagonal elements of E3 are zero. This computation can be made
with one of the LAPACK commands dgges and sgges. In version 7 and higher
of M ATLAB, the functions qz and ordqz can be used.
3. Solve the generalized Sylvester equation
E1 R + LE3 = −E2
(11.18a)
J1 R + LJ3 = −J2 .
(11.18b)
to get the matrices L and R. The generalized Sylvester equation (11.18) can be
solved from the linear equation system
− stack(E2 )
stack(R)
In ⊗ E1 E3T ⊗ Im
=
(11.19)
− stack(J2 )
stack(L)
In ⊗ J1 J3T ⊗ Im
or with the LAPACK commands stgsyl or dtgsyl. Here In is an identity
matrix with the same size as E3 and J3 , Im is an identity matrix with the same
size as E1 and J1 , ⊗ represents the Kronecker product and stack(X) denotes an
ordered stack of the columns of a matrix X from left to right starting with the first
column.
4. We now get the form (11.15) and (11.16) according to
−1
E1
I L
0
P =
P1
0 I
0
J3−1
I R
Q = Q1
0 I
(11.20a)
(11.20b)
N = J3−1 E3
(11.20c)
E1−1 J1
(11.20d)
A=
B
= P K.
D
(11.20e)
178
11
Implementation Issues
DC motor
Gearbox
Spring
Metal disc
Angle sensor
Figure 11.1: The examined process.
11.5
Application Example
In this section it is exemplified how the algorithms presented in this chapter can be used
when implementing parameter estimation for a physical process. The system setup examined is a DC-motor connected to a heavy metal disc through a gearbox and a spring,
see Figure 11.1. This setup simulates the problems that occur in power transfer in weak
axes, such as the rear axis in trucks. This problem is studied within the area of power train
control.
It should be stressed that the role of this example is to show how we can work with
DAE descriptions from Modelica-like modeling environments in estimation applications.
In this case, despite a singular E-matrix, the model will be reduced to a standard statespace description by the transformation mechanisms described in the earlier sections. The
properties of the actual estimates obtained will thus follow from well-known techniques
and results, and we will therefore not discuss accuracy aspects of the estimated models.
The laboratory process was modeled in Modelica. The model is linear, so the resulting
equations can be written on the form
E(θ)ẋ(t) = F (θ)x(t) + G(θ)u(t)
y(t) = H(θ)x(t).
(11.21a)
(11.21b)
The actual estimation was performed using M ATLAB. The transformation of the Modelica
model into DAE form in M ATLAB was performed manually, but the procedure could quite
easily be automated if it were possible to specify inputs, outputs, and unknown parameters
in Modelica. This is an important subject for future work, since gray-box identification
then could be performed by first modeling the system using Modelica, and then estimating
11.6
179
Difference-Algebraic Equations
unknown parameters and states in M ATLAB without having to manipulate any equations
manually.
To use, e.g., the System Identification Toolbox for M ATLAB (Ljung, 2006) to estimate
unknown parameters, the model was put into the idgrey object format. This means
that an m-file must be written, which, for each parameter vector produces the matrices
of a linear state-space system, A, B, C, D, K, x0. This means that this m-file will call
the transformation routines described previously in the chapter, which include calls to
functions from LAPACK. The model object is created by
mi = idgrey(’servo’,[10 -10],’c’,[], ...
0,’DisturbanceModel’,’None’);
and the model-defining m-file servo has the format
function [A,B,C,D,K,X0]=servo(pars,Tsm,Auxarg)
%Get DAE matrices E,F,G & H with
%parameters above
[E,F,G,H]=SpringServoMatrices(pars);
% Call to Lapack routine
[A,B,C,D]=StandardForm(E,F,G,H);
K=0; %Output error model
X0=0;
The function SpringServoMatrices computes DAE matrices corresponding to the
model structure (11.21) for a certain parameter value, and StandardForm computes
a corresponding state-space description using the methods discussed in this chapter. In
this case a well-defined state-space model is generated for all parameter values, so the
estimation command
m = pem(data,mi)
will work in a familiar fashion.
11.6
Difference-Algebraic Equations
The method for computing the canonical forms for difference-algebraic equations, or
discrete-time descriptor systems, is identical to the computation for continuous-time systems. This can be realized since the proofs of the transformations in the continuous-time
and discrete-time cases (Chapter 2) are similar and the computation for the continuoustime case is based on the proof of the transformation. For the summary in Section 11.4,
the only thing that changes is actually the first step. For the discrete-time case it takes the
following form:
1. Start with a system
Ex(t + 1) = Jx(t) + Ku(t)
y(t) = Lx(t)
(11.22a)
(11.22b)
180
11
that should be transformed into
I 0
A
Q−1 x(t + 1) =
0 N
0
Implementation Issues
0
B
Q−1 x(t) +
u(t)
I
D
(11.23)
or
x1 (t + 1) = Ax1 (t) + Bu(t)
x2 (t) = −Du(t) −
m−1
X
N i Du(t + i)
(11.24a)
(11.24b)
i=1
x1 (t)
= Q−1 x(t)
x2 (t)
x (t)
.
y(t) = LQ 1
x2 (t)
(11.24c)
(11.24d)
The steps 2–4 are identical to Section 11.4.
11.7
Conclusions
We examined how the canonical forms discussed in Section 2.3 can be computed with
numerical software. The calculation is based on tools for the solution of generalized
eigenvalue problems, so generalized eigenvalue problems where briefly discussed. Implementations of the tools for generalized eigenvalue problems are available in the free
LAPACK package.
12
Initialization of Parameter Estimates
Since DAE systems can be formed by simply writing down basic physical relations, the
matrix elements of linear DAE systems are often physical parameters or known constants.
This special structure is not used by the parameter estimation methods discussed in the
earlier chapters. In this chapter we will discuss how to utilize the structure that models in
DAE form often have to initialize parameter estimation methods.
12.1
Introduction
The parameter estimation methods discussed earlier have in common that they construct
a criterion function V (θ) that should be minimized to estimate the unknown parameters.
For the physically parameterized model structures discussed in this thesis, V (θ) is a complex function of the parameters θ. This means that the criterion function in general cannot
be minimized analytically. Instead, we have to resort to numerical search methods such
as Gauss-Newton as discussed by Ljung (1999). Such methods only guarantee convergence to a local minimum, and experience shows that it can be difficult to find the global
minimum of V (θ) for physically parameterized model structures. One remedy is to use
physical insight when selecting initial values for the numerical search, and another is to
do several searches with different starting values. Although these remedies can work well
in many cases, there is still no guarantee that the global optimum is found. In this chapter
we will therefore discuss how initial parameter values for the numerical search can be
chosen by minimization of a polynomial. The following example illustrates this.
Example 12.1: Initalization through transfer function coefficients.
Consider a body with mass m to which a force F (t) is applied. The motion of the body
is damped by friction with damping coefficient k. If x(t) is the position of the body,
the equation for the motion of the body is mẍ(t) = F (t) − k ẋ(t). The position is the
measured output of the model. With v(t) denoting the velocity of the body, this can be
181
182
12
written in DAE form as
1 0
ẋ(t)
0
=
0 m
v̇(t)
0
y(t) = 1
Initialization of Parameter Estimates
1
x(t)
0
+
F (t)
−k
v(t)
1
x(t)
0
.
v(t)
The transfer function for this system is
1 0
0
−
G(s, θ) = 1 0 s
0 m
0
1
−k
(12.1a)
(12.1b)
−1 1
0
.
=
2
1
ms + ks
(12.2)
If a black-box estimation procedure has given the transfer function
Ĝ(s) =
1
2s2 + 3s + 0.01
(12.3)
a polynomial which measures the difference of the transfer function coefficients is
p(θ) = (m − 2)2 + (k − 3)2 + 0.012 .
(12.4)
This polynomial is minimized by m = 2 and k = 3.
As shown in the example, we assume that a black-box model of the system has been
estimated beforehand by for example a subspace method (Ljung, 1999). The polynomial
is then formed as a measure of the “distance” between the black-box model and the physically parameterized model. Although the measure is formed in an ad hoc manner, it
should in many cases give a better initial value than a pure guess. However, we will have
no guarantees for the quality of the initial values selected. Therefore the results should be
compared to the results for initial values selected from physical insight, or for randomly
selected initial values.
We saw in Example 12.1, that if the black-box model and the physically parameterized
model both are in transfer function form, one straightforward way to get initial values for
the parameter search is to try to make the coefficients of the numerator and denominator
polynomials as equal as possible. Note that linear state-space and linear DAE models
easily can be converted to transfer functions as discussed earlier.
Although the polynomial p(θ) in Example 12.1 was trivial to minimize, one can note
that p(θ) can be a high order polynomial with as many variables as there are unknown
parameters. In some cases it could be preferable to have a polynomial with a lower degree,
but with a higher number of variables. For parameterized state-space systems, Parrilo and
Ljung (2003) discusses a method to find a polynomial which is biquadratic in its variables
(this work is based on the paper by Xie and Ljung, 2002). This method requires that
the elements of the state-space matrices are unknown parameters or constants. It is also
proposed that the polynomial could be minimized by sum of squares optimization. The
price to get a biquadratic polynomial to minimize is that more variables than the unknown
parameters must be included in the polynomial.
The requirement that the elements of the state-space matrices should be unknown
physical parameters or known constants can be rather strict. Since one usually needs
12.2
183
Transforming the Problem
to make different transformations to get a state-space description, the elements of the
matrices are usually functions of the unknown physical parameters. It is much more
likely that the elements of the matrices of a linear DAE system are unknown parameters
or constant, since basic physical equations often are simple integrators and static relations.
By applying the technique from Parrilo and Ljung (2003) to linear DAE systems, we can
therefore utilize the structure that often is present in linear DAE systems. This is what
this chapter is about. We will also discuss sum of squares optimization (Parrilo, 2000;
Prajna et al., 2004), which in some cases can be used to find the global minimum of a
polynomial.
That linear DAE systems often have simple structure is also motivated by Example 12.2 below.
Example 12.2: DAE model versus state-space model
Consider the system in Example 12.1:
ẋ(t)
0 1
x(t)
0
1 0
=
+
F (t)
v̇(t)
0 −k
v(t)
1
0 m
(12.5)
In DAE form, the elements of the matrices are clearly known or physical parameters.
However, this is not the case if the system is written in state-space form:
0
1
0
ẋ(t)
x(t)
=
+ 1 F (t)
(12.6)
k
v̇(t)
v(t)
0 −m
m
12.2
Transforming the Problem
In this section, we describe how the problem of finding initial values for the parameters to
be estimated can be posed as the minimization of a biquadratic polynomial. The transformation is based on that we have a consistently estimated black-box model in state-space
form,
ẋ(t) = A0 x(t) + B0 u(t)
y(t) = C0 x(t),
(12.7a)
(12.7b)
which could have been estimated using for example a subspace method (Ljung, 1999).
The idea is then that there should exist a transformation between the parameterized DAE
model and the black-box model for the optimal parameter values. Because of modeling
errors and noise, there will typically not exist an exact transformation between the systems, and we therefore choose to minimize a norm which measures the difference between
the two systems as a function of the parameters.
As the transformations are simplified considerably in the special case when E(θ) is
invertible, this case is discussed separately in section 12.2.1. The general case is discussed
in section 12.2.2.
184
12.2.1
12
Initialization of Parameter Estimates
The Case of Invertible E(θ)
Consider the DAE system
E(θ)ẋ(t) = J(θ)x(t) + K(θ)u(t)
y(t) = L(θ)x(t)
(12.8a)
(12.8b)
and let E(θ) be invertible. Lemma 2.3 gives that a transformation
P E(θ)QQ−1 ẋ(t) = P J(θ)QQ−1 x(t) + P K(θ)u(t)
−1
y(t) = L(θ)QQ
x(t)
(12.9a)
(12.9b)
with invertible P and Q results in a state-space description,
ż(t) = P J(θ)Qz(t) + P K(θ)u(t)
y(t) = L(θ)Qz(t)
(12.10a)
(12.10b)
x(t) = Qz(t).
(12.10c)
It is clear that it is possible to achieve all state-space descriptions that are equivalent
to (12.8) in this way by including a further similarity transformation of the state-space
description in P and Q.
If we now have a consistent estimate of the system in the form (12.7), we want to find
parameter values θ that make the input-output behavior of (12.7) and (12.8) as equal as
possible. If it were possible to make them exactly equal, there would be matrices P and
Q and parameter values θ̂ such that
P E(θ̂)Q = I
(12.11a)
P J(θ̂)Q = A0
(12.11b)
P K(θ̂) = B0
(12.11c)
L(θ̂)Q = C0
(12.11d)
which also can be written as
P E(θ̂) = Q−1
(12.12a)
−1
P J(θ̂) = A0 Q
(12.12b)
P G(θ̂) = B0
(12.12c)
−1
L(θ̂) = C0 Q
.
(12.12d)
As there will always be some noise and modeling errors, we cannot expect these equations
to hold exactly. Therefore we form a polynomial that measures how well these equations
are satisfied:
p1 (θ, P, Q−1 ) = kP E(θ) − Q−1 k2F
+ kP J(θ) − A0 Q−1 k2F
+ kP G(θ) − B0 k2F
+ kL(θ) − C0 Q−1 k2F
(12.13)
12.2
185
Transforming the Problem
Here k · k2F denotes the squared Frobenius norm, i.e., the sum of all squared matrix elements. This polynomial is always biquadratic in the unknown parameters θ and the
elements of the matrices P and Q−1 , if the elements of the DAE matrices are constants
or unknown parameters. When the polynomial is formed as in Example 12.1, the polynomial is not guaranteed to be biquadratic, but could have higher degree. The method in
this section consequently guarantees that the polynomial to be minimized is biquadratic
to the prize of a higher number of variables. If minimization of (12.13) does not give good
results, one may instead try to minimize
p2 (θ, P −1 , Q) = kE(θ)Q − P −1 k2F
+ kJ(θ)Q − P −1 A0 k2F
+ kG(θ) − P −1 B0 k2F
+ kL(θ)Q − C0 k2F .
(12.14)
This polynomial is biquadratic in the unknown parameters θ and the elements of the matrices P −1 and Q if the elements of the DAE matrices are constants or unknown parameters.
It also measures how well (12.11) is satisfied.
12.2.2
The Case of Non-Invertible E(θ)
In the case when E(θ) is not invertible, it is still possible to formulate a polynomial
that can give good initial values for the parameter search when minimized. However, in
this more complex case, it cannot in general be guaranteed that the polynomial will be
biquadratic in the unknown variables. Therefore we will also discuss additional assumptions to achieve this.
As the output of DAE systems can depend on derivatives of the input, we must assume
that the estimated black-box model of the system is in the form
ẋ(t) = A0 x(t) + B0 u(t)
y(t) = C0 x(t) +
m−1
X
D0k u(k) (t).
(12.15a)
(12.15b)
k=0
Furthermore, we know from Lemma 2.3 that for each selection of parameter values θ
there exists a transformation
P E(θ)QQ−1 ẋ(t) = P J(θ)QQ−1 x(t) + P K(θ)u(t)
−1
y(t) = L(θ)QQ
x(t)
that gives the system
I 0
ẋ1 (t)
A 0
x1 (t)
B
=
+
u(t)
0 N
ẋ2 (t)
0 I
x2 (t)
D
x1 (t)
= Q−1 x(t)
x2 (t)
x (t)
y(t) = L(θ)Q 1
.
x2 (t)
(12.16a)
(12.16b)
(12.17a)
(12.17b)
(12.17c)
186
12
Initialization of Parameter Estimates
According to Theorem 2.3 this can be further transformed into the form
ẋ1 (t) = Ax1 (t) + Bu(t)
x2 (t) = −Du(t) −
m−1
X
N i Du(i) (t)
(12.18a)
(12.18b)
i=1
x1 (t)
= Q−1 x(t)
x2 (t)
x1 (t)
y(t) = L(θ)Q
.
x2 (t)
(12.18c)
(12.18d)
We now want to find parameter values θ and transformation matrices P and Q such that
the models (12.15) and (12.18) have the same input-output behavior. From (12.15)–
(12.18), we see that this is the case if the following equations are satisfied.
I 0
P E(θ)Q =
(12.19a)
0 N
A0 0
(12.19b)
P J(θ)Q =
0 I
B0
(12.19c)
P K(θ) =
D
L(θ)Q = C0 C2
(12.19d)
D00 = −C2 D
(12.19e)
D0k = −C2 N k D, k = 1 . . . m − 1.
(12.19f)
Nm = 0
(12.19g)
Here we introduced the matrix C2 to simplify the notation. Equation (12.19g) guarantees
that N is nilpotent. This can also be achieved by for example parameterizing N as an
upper triangular matrix with zero diagonal elements, but then extra care would have to be
taken to guarantee that N is nilpotent of the correct order. A polynomial that measures
how well these equations are satisfied can now be formed:
I 0
p3 (θ, P, Q−1 , N, D, C2 ) = kP E(θ) −
Q−1 k2F
(12.20)
0 N
A0 0
+ kP J(θ) −
Q−1 k2F
0 I
B0 2
+ kP K(θ) −
kF
D
+ kL(θ) − C0 C2 Q−1 k2F
+ kD00 + C2 Dk2F
+
+
m−1
X
kD0k + C2 N k Dk2F
k=1
kN m k2F
12.3
187
Sum of Squares Optimization
This polynomial can unfortunately not be guaranteed to be biquadratic in its variables
(the elements of θ and the unknown matrices), even if the elements of the DAE matrices
are constants or unknown parameters. However, if the true system has
D0k = 0, k = 0 . . . m − 1
(12.21)
and the DAE model is such that
C2 D = 0
(12.22a)
k
C2 N D = 0, k = 1 . . . m − 1
Nm = 0
(12.22b)
(12.22c)
then (12.20) simplifies to
−1
p4 (θ, P, Q
I 0
, N, D, C2 ) = kP E(θ) −
Q−1 k2F
0 N
A0 0
Q−1 k2F
+ kP J(θ) −
0 I
B0 2
kF
+ kP K(θ) −
D
−1 2
+ kL(θ) − C0 C2 Q kF .
(12.23)
This polynomial is biquadratic in its variables.
The relation (12.21) can in many cases be physically motivated, since it is common
that the output of physical systems does not depend directly on the input or its derivatives.
If this is the case, the DAE matrices should be parameterized so that the (12.22) holds for
all or almost all parameter values. Note that it always can be tested afterwards if (12.22)
is fulfilled. This is simply done by testing if C2 D = 0, if C2 N k D = 0, and if N m = 0.
12.3
Sum of Squares Optimization
The polynomials that are formed in this chapter could be minimized by any method that
gives the global minimum. One family of methods that could be used are algebraic methods, such as Gröbner bases. Here we will discuss another method which relaxes the minimization to a sum of squares problem, as described by, e.g., Parrilo (2000) and Prajna
et al. (2004). To describe this procedure, we first need to note that the problem
min p(θ)
(12.24)
θ
also can be written as
max
λ
subject to p(θ) − λ ≥ 0
(12.25a)
for all θ.
(12.25b)
188
12
Initialization of Parameter Estimates
Now, since a sum of squared real polynomials fi (θ, λ) always is greater than or equal to
zero, a relaxation of (12.25) is
max
subject to
λ
p(θ) − λ =
(12.26a)
X
fi2 (θ, λ).
(12.26b)
i
As described in the references, the relaxed problem always gives a lower bound on the optimal value, and for many problems it also attains a strict lower bound. The relaxed problem can be solved using semidefinite programming as described by Prajna et al. (2004).
The algorithms for finding the lower bound also often find variable values that attain this
lower bound, and in this case we of course have the actual optimum.
The reason that the algorithm gives a lower bound, which is not guaranteed to be the
actual optimum, is that non-negativity of a polynomial is not equivalent to the polynomial
being a sum of squares. However, in the following cases non-negativity and the existence
of a sum of squares decomposition are equivalent (Prajna et al., 2004):
• Univariate polynomials of any (even) degree.
• Quadratic polynomials in any number of variables.
• Quartic polynomials in two variables.
Unfortunately, the polynomials we have formed are biquadratic, so we are not guaranteed to find the minimum. If the optimal value is zero we will anyway have equivalence
between non-negativity and the existence of a sum of squares decomposition since the
original formulation of the polynomials, since (12.13), (12.14), (12.20), or (12.23) then
are suitable sum of square decompositions for λ = 0. We will have this case if there exists
a parameter value that make the input-output behavior of the DAE system and black-box
model exactly equal.
12.4
Difference-Algebraic Equations
The discussion in Section 12.2 is valid also for the discrete-time case. The only difference
is that we have a difference-algebraic equation
E(θ)x(t + 1) = J(θ)x(t) + K(θ)u(t)
y(t) = L(θ)x(t)
(12.27a)
(12.27b)
for which we need to find initial values for parameter estimation. In the case when E(θ)
is invertible, we assume that a consistently estimated black-box model
x(t + 1) = A0 x(t) + B0 u(t)
y(t) = C0 x(t)
(12.28a)
(12.28b)
is available. The polynomials p1 and p2 in (12.13) and (12.14) can then be minimized to
find initial parameter values. In the case where E(θ) is not invertible, we instead assume
12.5
189
Conclusions
that a black-box model according to
x(t + 1) = A0 x(t) + B0 u(t)
y(t) = C0 x(t) +
m−1
X
D0k u(t + k)
(12.29a)
(12.29b)
k=0
is available. The polynomial p3 in (12.20) can then be used to find initial values. If the
assumptions (12.21) and (12.22) are fulfilled, the simpler polynomial p4 in (12.23) can be
used instead.
12.5
Conclusions
We noted that the standard system identification problem often is a minimization with
many local minima. As the minimization problem normally is solved using a standard
optimization method, it is important to have initial values near the optimal values of parameters that are to be estimated. We noted that a polynomial which measures the difference between the coefficients of transfer functions can be formed. If this polynomial
is minimized, it should give good initial values. However, this polynomial can have a
high degree, so we examined how a polynomial which is biquadratic can be formed. This
polynomial also gives an initial guess for the parameters if it is minimized, but has more
unknown variables. To guarantee that this polynomial is biquadratic, we used the special
structure that often is present in linear DAE systems.
190
12
Initialization of Parameter Estimates
13
Conclusions
A large part of this thesis has been devoted to noise modeling in DAE models. For nonlinear DAE models, sufficient conditions for well-posedness of stochastic models were
developed. For the linear case, a more thorough analysis could be performed to develop
both necessary and sufficient conditions for well-posedness of models and different estimation problems.
The motivation to study DAE models was mainly that component-based modeling,
such as in Modelica, leads to DAE models. The motivation for studying noise models, is
that they allow implementation of estimation methods that have proven to be effective for
estimation of time dependent variables and time invariant parameters. It was consequently
also discussed how the stochastic DAE models could be used for particle filtering, Kalman
filtering, and parameter estimation using the maximum likelihood and prediction error
methods. It was also suggested how the methods could be implemented. For nonlinear
DAE models, it was suggested to use DAE solvers. Here further work could be directed
at utilizing the structure of the equations to speed up the computations. For linear models,
it was discussed how the methods in the thesis can be implemented using tools from the
linear algebra package LAPACK.
We have also examined model properties such as observability and identifiability,
which are important in connection with parameter and state estimation. These properties were studied for nonlinear DAE models, but the results are of course valid also for
linear models. A basic idea was to formulate the observability problem itself as a DAE,
and examine the properties of that DAE. As we have seen, this idea can also be used for
example to examine zero dynamics. An interesting topic to future research is to examine
if this idea can be used for analysis of other model properties as well.
For linear models, the problem of finding initial values for parameter estimation procedures was briefly discussed. This is an important topic where more research is necessary,
both for linear and nonlinear models.
191
192
13
Conclusions
Appendices
193
A
Notation
Symbols and Mathematical Notation
R
The set of real numbers
t
ẋ(t)
ẍ(t)
px(t)
qx(tk )
Time variable
d
Derivative of the function x(t) with respect to time, dt
x(t)
The second derivative of the function x(t) with respect to
d2
time, dt
2 x(t)
The n:th derivative of the function x(t) with respect to
dn
time, dt
n x(t)
d
x(t)
Derivative of the function x(t) with respect to time, dt
Shift operator, qx(tk ) = x(tk+1 )
δ(t)
δtk ,ts
arg min f (x)
Generalized Dirac function
δtk ,ts = 1 if tk = ts , δtk ,ts = 0 otherwise
The value of x that minimizes the function f (x)
x(n) (t)
x
arg max f (x)
The value of x that maximizes the function f (x)
x
rank A
corank A
kernel A
N (A)
cokernel A
range A
V(A)
corange A
Rank of the matrix A
Corank of the matrix A
Kernel (null space) of the matrix A
Kernel (null space) of the matrix A
Cokernel of the matrix A
Range of the matrix A
Range of the matrix A
Corange of the matrix A
195
196
A
det(·)
⊗
stack(·)
The determinant of the argument
Kronecker product
An ordered stack of the columns of the (matrix) argument
from left to right starting with the first column
Conjugate transpose of the matrix A
Identity matrix of appropriate dimensions
Dimension of the vector x
Oblique projection of the matrix A along the space B on
the space C
A∗
I
dim x
A/B C
θ
DM
ZN
ŷ(tk |tk−1 , θ)
ε(tk , θ)
E(x)
Pr
cov(x, y)
var(x)
rxy (s, t)
Vector of unknown variables in a system identification
problem
Set in which the parameters θ lie
Measured data, {u(t0 ), y(t0 ), ..., u(tN ), y(tN )} or
{U (ω1 ), Y (ω1 ), ..., U (ωN ), Y (ωN )}
A model’s prediction of y(tk ) given θ and Z k−1
Prediction error, y(tk ) − ŷ(tk |tk−1 , θ)
L[·]
Z[·]
Expected value of the stochastic variable x
Probability
Covariance for the stochastic variables x and y
Variance of the stochastic variable x
Covariance function for the stochastic processes x(s) and
y(t)
Laplace transform of the argument
Z transform of the argument
Fl
Fl;p
Derivative array
Partial derivatives of Fl with respect to the variables p
Acronyms
DAE
MFD
MSS
RMSE
SDAE
SDE
SVD
Differential-algebraic equation
Matrix fraction description
Minimally structurally singular
Root mean square error
Stochastic differential-algebraic equation
Stochastic differential equation
Singular value decomposition
Notation
B
Proof of Theorem 9.1
In this appendix Theorem 9.1 is proved. Recall that λ(θ) is a scalar such that λ(θ)E(θ) +
F (θ) is invertible and
−1
Ē(θ) = λ(θ)E(θ) + F (θ)
E(θ).
(B.1)
First we will prove two propositions:
Proposition B.1
Consider the SDAE (9.1) with the matrix Ē(θ) transformed into Jordan form:
Es (θ)
−1
0
T1 (θ) T2 (θ)
Ē(θ) = T1 (θ) T2 (θ)
0
N (θ)
|
{z
}
(B.2)
T (θ)
where the zero eigenvalues are sorted to the lower right so that Es is invertible and N is
nilpotent of order m (N m = 0, N m−1 6= 0).
Then the transformation
xs
x = T1 (θ) T2 (θ)
(B.3)
{z
} xa
|
T (θ)
gives a system description of the form
nw
X
Es (θ)ẋs = I − λ(θ)Es (θ) xs + Gs (θ)u +
Jl,s (θ)wl (θ)
N (θ)ẋa = I − λ(θ)N (θ) xa + Ga (θ)u +
l=1
nw
X
l=1
197
Jl,a (θ)wl (θ)
(B.4a)
(B.4b)
198
B
Proof of Theorem 9.1
where
−1
Jl,s (θ)
= T −1 (θ) λ(θ)E(θ) + F (θ)
Jl (θ)
Jl,a (θ)
−1
Gs (θ)
= T −1 (θ) λ(θ)E(θ) + F (θ)
G(θ).
Ga (θ)
(B.5)
(B.6)
Proof: Adding λ(θ)E(θ)x to each side of Equation (9.1a) and then multiplying from the
left with (λE(θ) + F (θ))−1 gives
!
nw
X
−1
Ē(θ) ẋ + λ(θ)x = x + λ(θ)E(θ) + F (θ)
× G(θ)u +
Jl (θ)wl (θ) .
l=1
Substituting
xs
x = T (θ)
xa
(B.7)
and multiplying from the left with T −1 (θ) gives
xs
ẋs
T −1 (θ)Ē(θ)T (θ)
=
+λ
xa
ẋa
−1
xs
+ T −1 (θ) λE(θ) + F (θ)
×
xa
G(θ)u +
nw
X
!
Jl (θ)wl (θ)
l=1
which is the desired form.
Proposition B.2
The auxiliary variables xa can be solved from (B.4b) to give
d
xa = − I +
+ λ(θ) N (θ) + · · · +
dt
nw
d
m−1
X
+ λ(θ)
N m−1 (θ) × Ga (θ)u +
Jl,a (θ)wl (θ)
dt
(B.8)
l=1
Proof: Writing (B.4b) as
nw
X
d
+ λ(θ) xa − Ga (θ)u +
Jl,a (θ)wl (θ)
xa = N (θ)
dt
(B.9)
l=1
d
and successively “multiplying” by N (θ)( dt
+ λ) gives (omitting dependence on θ)
2
nw
X
d
d
d
N
+ λ xa = N 2
+ λ xa − N
+ λ Ga u +
Jl,a wl (θ)
dt
dt
dt
l=1
N m−1
d
+λ
dt
m−1
..
.
xa = −N m−1
d
+λ
dt
m−1 Ga u +
nw
X
l=1
Jl,a wl
199
where we have used N m = 0 in the last equation. A successive substitution from these
equations into (B.9) then gives (B.8).
We now prove the main result, Theorem 9.1.
Proof: Transforming the system into the form (B.4) we see that xs is an integration of
the second order processes wl (θ). Hence, it has finite variance. Since
H(θ)x = H(θ)T1 (θ)xs + H(θ)T2 (θ)xa
it must also be required that H(θ)T2 (θ)xa has finite variance. Note that wl (θ) has finite
variance if it is differentiated at most pl − 1 times since it has pole excess 2pl . This can
dn
be realized from (2.179b) which gives that the variance of dt
n wl (θ) is
Z∞
r(0) =
−∞
(
<∞
(iw) φwl dω
=∞
2n
if n ≤ pl − 1
if n ≥ pl .
(B.10)
(B.8) thus gives that H(θ)T2 (θ)xa has finite variance if and only if
H(θ)T2 (θ)N j (θ)Jl,a (θ) = 0
j ≥ pl , ∀l.
(B.11)
By using the notation [·]/X Y for the oblique projection on the space Y along the space
X and V(A) for the space spanned by the columns of the matrix A, this condition can be
written as (omitting dependence on θ)
0 = HT2 N j Jl,a
−1 = H 0 T2 T1 T2
T1 Esj Jl,s + T2 N j Jl,a
= H T1 Esj Jl,s + T2 N j Jl,a V(T ) V(T2 )
1
Esj
0
Jl,s
V(T2 )
= H T1 T2
0 Nj
Jl,a
V(T1 )
= H Ē j (λE + F )−1 Jl V(T ) V(T2 ).
1
(B.12)
Since Es (θ) is invertible and N (θ) is nilpotent, (B.2) gives that V(T2 (θ)) = N (Ē n (θ))
and that V(T1 (θ)) = V(Ē n (θ)), so the condition can also be written
h
i.
−1
N Ē n (θ) ∈ N (H(θ)) j ≥ pl , ∀l.
Ē j (θ) λ(θ)E(θ) + F (θ)
Jl (θ)
V Ē n (θ)
200
B
Proof of Theorem 9.1
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Index
autocovariance function, 47
Brownian motion, 47
cokernel, 39
corange, 39
corank, 18
covariance function, 47
DAE
linear, 24
linear time-varying, 38
nonlinear, 13
regular, 23, 25, 41, 44
sampling, 36
solvable, 15
solver, 41
state-space form, 33
derivative array, 16
difference-algebraic equation, 43
differential algebra, 59
differential-algebraic equation, see DAE
Dymola, 8
identifiability, 58
DAE, 87, 105, 119
implementation, 77, 173
impulse controllability, 136
index
differential, 14
strangeness, 19
initial condition
consistent, 15
kernel, 39
Kunkel and Mehrmann, 15
LAPACK, 173
maximum likelihood method, 57
minimally structurally singular, 123
model, 7
component-based, 7
deterministic, 8
gray-box, 55
stochastic, 12, 46
Modelica, 7
frequency domain identification, 57
DAE, 157
nilpotent, 197
null-space, 39
Gaussian process, 47
generalized eigenvalue, 174
oblique projection, 153
observability, 61
207
208
DAE, 87
observability indeces, 99
OpenModelica, 8
Pantelides’s algorithm, 41
parameter estimation, see system identification
particle filter, 73
pole excess, 68
prediction error method, 55
range, 39
regularity, see DAE, regular
row degree, 140
row reduced, 140
SDAE
linear, 133
nonlinear, 65
sampling, 144
shuffle algorithm, 31
spectral density, 48
spectrum, 48
state estimation
linear DAE, 163
nonlinear DAE, 73
well-posed problem, 164, 167
state-space model, 11
stationary process, 47
stochastic process, 47
sum of squares optimization, 187
SVD coordinate system, 32
system identification, 55
initialization, 180
linear DAE, 151
nonlinear DAE, 82
well-posed problem, 152, 159
U-indistinguishable, 61
Wiener process, 47
zero dynamics, 101
Index
PhD Dissertations
Division of Automatic Control
Linköpings universitet
M. Millnert: Identification and control of systems subject to abrupt changes. Thesis No. 82, 1982.
ISBN 91-7372-542-0.
A. J. M. van Overbeek: On-line structure selection for the identification of multivariable systems.
Thesis No. 86, 1982. ISBN 91-7372-586-2.
B. Bengtsson: On some control problems for queues. Thesis No. 87, 1982. ISBN 91-7372-593-5.
S. Ljung: Fast algorithms for integral equations and least squares identification problems. Thesis
No. 93, 1983. ISBN 91-7372-641-9.
H. Jonson: A Newton method for solving non-linear optimal control problems with general constraints. Thesis No. 104, 1983. ISBN 91-7372-718-0.
E. Trulsson: Adaptive control based on explicit criterion minimization. Thesis No. 106, 1983.
ISBN 91-7372-728-8.
K. Nordström: Uncertainty, robustness and sensitivity reduction in the design of single input control systems. Thesis No. 162, 1987. ISBN 91-7870-170-8.
B. Wahlberg: On the identification and approximation of linear systems. Thesis No. 163, 1987.
ISBN 91-7870-175-9.
S. Gunnarsson: Frequency domain aspects of modeling and control in adaptive systems. Thesis
No. 194, 1988. ISBN 91-7870-380-8.
A. Isaksson: On system identification in one and two dimensions with signal processing applications. Thesis No. 196, 1988. ISBN 91-7870-383-2.
M. Viberg: Subspace fitting concepts in sensor array processing. Thesis No. 217, 1989. ISBN 917870-529-0.
K. Forsman: Constructive commutative algebra in nonlinear control theory. Thesis No. 261, 1991.
ISBN 91-7870-827-3.
F. Gustafsson: Estimation of discrete parameters in linear systems. Thesis No. 271, 1992.
ISBN 91-7870-876-1.
P. Nagy: Tools for knowledge-based signal processing with applications to system identification.
Thesis No. 280, 1992. ISBN 91-7870-962-8.
T. Svensson: Mathematical tools and software for analysis and design of nonlinear control systems.
Thesis No. 285, 1992. ISBN 91-7870-989-X.
S. Andersson: On dimension reduction in sensor array signal processing. Thesis No. 290, 1992.
ISBN 91-7871-015-4.
H. Hjalmarsson: Aspects on incomplete modeling in system identification. Thesis No. 298, 1993.
ISBN 91-7871-070-7.
I. Klein: Automatic synthesis of sequential control schemes. Thesis No. 305, 1993. ISBN 917871-090-1.
J.-E. Strömberg: A mode switching modelling philosophy. Thesis No. 353, 1994. ISBN 91-7871430-3.
K. Wang Chen: Transformation and symbolic calculations in filtering and control. Thesis No. 361,
1994. ISBN 91-7871-467-2.
T. McKelvey: Identification of state-space models from time and frequency data. Thesis No. 380,
1995. ISBN 91-7871-531-8.
J. Sjöberg: Non-linear system identification with neural networks. Thesis No. 381, 1995. ISBN 917871-534-2.
R. Germundsson: Symbolic systems – theory, computation and applications. Thesis No. 389,
1995. ISBN 91-7871-578-4.
P. Pucar: Modeling and segmentation using multiple models. Thesis No. 405, 1995. ISBN 917871-627-6.
H. Fortell: Algebraic approaches to normal forms and zero dynamics. Thesis No. 407, 1995.
ISBN 91-7871-629-2.
A. Helmersson: Methods for robust gain scheduling. Thesis No. 406, 1995. ISBN 91-7871-628-4.
P. Lindskog: Methods, algorithms and tools for system identification based on prior knowledge.
Thesis No. 436, 1996. ISBN 91-7871-424-8.
J. Gunnarsson: Symbolic methods and tools for discrete event dynamic systems. Thesis No. 477,
1997. ISBN 91-7871-917-8.
M. Jirstrand: Constructive methods for inequality constraints in control. Thesis No. 527, 1998.
ISBN 91-7219-187-2.
U. Forssell: Closed-loop identification: Methods, theory, and applications. Thesis No. 566, 1999.
ISBN 91-7219-432-4.
A. Stenman: Model on demand: Algorithms, analysis and applications. Thesis No. 571, 1999.
ISBN 91-7219-450-2.
N. Bergman: Recursive Bayesian estimation: Navigation and tracking applications. Thesis
No. 579, 1999. ISBN 91-7219-473-1.
K. Edström: Switched bond graphs: Simulation and analysis. Thesis No. 586, 1999. ISBN 917219-493-6.
M. Larsson: Behavioral and structural model based approaches to discrete diagnosis. Thesis
No. 608, 1999. ISBN 91-7219-615-5.
F. Gunnarsson: Power control in cellular radio systems: Analysis, design and estimation. Thesis
No. 623, 2000. ISBN 91-7219-689-0.
V. Einarsson: Model checking methods for mode switching systems. Thesis No. 652, 2000.
ISBN 91-7219-836-2.
M. Norrlöf: Iterative learning control: Analysis, design, and experiments. Thesis No. 653, 2000.
ISBN 91-7219-837-0.
F. Tjärnström: Variance expressions and model reduction in system identification. Thesis No. 730,
2002. ISBN 91-7373-253-2.
J. Löfberg: Minimax approaches to robust model predictive control. Thesis No. 812, 2003.
ISBN 91-7373-622-8.
J. Roll: Local and piecewise affine approaches to system identification. Thesis No. 802, 2003.
ISBN 91-7373-608-2.
J. Elbornsson: Analysis, estimation and compensation of mismatch effects in A/D converters.
Thesis No. 811, 2003. ISBN 91-7373-621-X.
O. Härkegård: Backstepping and control allocation with applications to flight control. Thesis
No. 820, 2003. ISBN 91-7373-647-3.
R. Wallin: Optimization algorithms for system analysis and identification. Thesis No. 919, 2004.
ISBN 91-85297-19-4.
D. Lindgren: Projection methods for classification and identification. Thesis No. 915, 2005.
ISBN 91-85297-06-2.
R. Karlsson: Particle Filtering for Positioning and Tracking Applications. Thesis No. 924, 2005.
ISBN 91-85297-34-8.
J. Jansson: Collision Avoidance Theory with Applications to Automotive Collision Mitigation.
Thesis No. 950, 2005. ISBN 91-85299-45-6.
E. Geijer Lundin: Uplink Load in CDMA Cellular Radio Systems. Thesis No. 977, 2005.
ISBN 91-85457-49-3.
M. Enqvist: Linear Models of Nonlinear Systems. Thesis No. 985, 2005. ISBN 91-85457-64-7.
T. B. Schön: Estimation of Nonlinear Dynamic Systems — Theory and Applications. Thesis
No. 998, 2006. ISBN 91-85497-03-7.
I. Lind: Regressor and Structure Selection — Uses of ANOVA in System Identification. Thesis
No. 1012, 2006. ISBN 91-85523-98-4.
J. Gillberg: Frequency Domain Identification of Continuous-Time Systems Reconstruction and
Robustness. Thesis No. 1031, 2006. ISBN 91-85523-34-8.
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