full text pdf

full text pdf
10.1515/msr-2015-0040
MEASUREMENT SCIENCE REVIEW, Volume 15, No. 6, 2015
A Robust Method of Vehicle Stability Accurate Measurement
Using GPS and INS
Zhibin Miao1, Hongtian Zhang1, Jinzhu Zhang2
1
College of Power and Energy, Harbin Engineering University, Harbin 150001, China,
[email protected]; [email protected]
2
Heilongjiang Institute of Technology, Harbin 150050, China, [email protected]
With the development of the vehicle industry, controlling stability has become more and more important. Techniques of
evaluating vehicle stability are in high demand. Integration of Global Positioning System (GPS) and Inertial Navigation System
(INS) is a very practical method to get high-precision measurement data. Usually, the Kalman filter is used to fuse the data from
GPS and INS. In this paper, a robust method is used to measure vehicle sideslip angle and yaw rate, which are two important
parameters for vehicle stability. First, a four-wheel vehicle dynamic model is introduced, based on sideslip angle and yaw rate.
Second, a double level Kalman filter is established to fuse the data from Global Positioning System and Inertial Navigation
System. Then, this method is simulated on a sample vehicle, using Carsim software to test the sideslip angle and yaw rate. Finally,
a real experiment is made to verify the advantage of this approach. The experimental results showed the merits of this method of
measurement and estimation, and the approach can meet the design requirements of the vehicle stability controller.
Keywords: Vehicle stability, Kalman filter, data fusion, sideslip angle, GPS/INS.
1. INTRODUCTION
W
ITH THE DEVELOPMENT of vehicle technology
and the improvement of road traffic, automobiles are
going faster and faster. There is a gradual increase in
the role of high-speed instability as a factor in all kinds of
traffic accidents. Audi company statistics indicate that with
traffic accidents involving vehicles at speeds of 80 km/h to
100 km/h, there was a loss of stability in 40 % of the cases
[1]. When the vehicle speed exceeds 160 km/h, almost all
accidents are related to vehicle instability. Related studies
also indicate that in serious traffic accidents caused by the
loss of stability control, 82 % of vehicles will travel 40
meters after the loss of control. A Toyota Corporation [2]
study also points out that the reason for almost accidents
caused by the loss of control involves the vehicle sideslip
motion. Therefore, stability control for vehicles is proposed.
Vehicle handling stability is improved by controlling vehicle
yaw motion.
Vehicle sideslip angle and yaw rate are two important
parameters for vehicle stability [3]. Vehicle sideslip angle is
the angle between the longitudinal axes of the automobile
body and the automobile speed direction. However,
nowadays the sideslip angle cannot be measured directly.
This is one of the biggest problems for the current
development of vehicle stability control system. Therefore,
it is the premise and key technology of vehicle dynamics
stability control to measure accurately the actual vehicle
sideslip angle and yaw rate [4]. Gyroscope can measure the
yaw rate, but there is no suitable equipment for directly
measuring the vehicle sideslip angle. Estimation methods
are used to get the sideslip angle. These methods are usually
combined with the use of the yaw rate gyroscope and lateral
acceleration sensor. However, these sensors usually contain
a bias and noise. In addition, a lateral accelerometer cannot
provide a good identification of vehicle lateral acceleration
and gravity component of acceleration [5]. The errors of
these sensors will be accumulated and even divergent, when
the integral is applied. Therefore, errors affect the
performance of vehicle stability control system. However,
the application of GPS and INS (inertial navigation system,
including the gyroscope and accelerometer) can directly
measure the vehicle sideslip angle. GPS is Global
Positioning System. DGPS (Differential Global Positioning
System) is a kind of GPS attached to the normal differential
correction signal, improving data precision to millimeter
level after processing.
There is a strong complementary link between GPS and
INS. GPS has some disadvantages. For example, a receiver
antenna may be blocked temporarily or it may lose position
data due to a signal interruption. INS can provide position;
velocity and azimuth information without an external
reference source, but the system has accumulated error. It
cannot give high-accuracy positioning information for a
long time because of gyroscope drift error. Errors of INS are
mainly random drift errors which cannot be compensated.
GPS has such advantages as high positioning accuracy and
no accumulated error. The two kinds of system, used in
combination, can compensate for their respective limitations
and give full play to their strengths. GPS measurement is
stable but the refresh rate (1~10 Hz) is relatively low.
GPS/INS integrated navigation system is a kind of
composite that has unique advantages in terms of autonomy
and bandwidth frequency Kalman filter is commonly used to
fuse GPS/INS data.
GPS/INS is a trend to measure vehicle movement stability
in modern automobile technology. At present, GPS
measurement has a low refresh data rate, and sometimes
there are obstacles that prevent vehicles from accepting GPS
information. Therefore, GPS and inertial sensor combination
application is needed. There are Kalman federated filtering
algorithms, D-S evidence theory, neural network, adaptive
H filtering, and fuzzy logic in data fusion. But each
algorithm has limitations. Therefore, more convenient and
high precision data fusion algorithm is a meaningful
problem.
294
Unauthenticated
Download Date | 9/19/17 10:59 AM
MEASUREMENT SCIENCE REVIEW, Volume 15, No. 6, 2015
2. RELATED WORK
In a vehicle stability measurement system, GPS can be
used to detect performance. It can also be used as a sensor to
provide real-time information for a vehicle dynamics
stability control system. Now GPS has been applied in
vehicle dynamics stability testing instruments, and the study
of GPS/INS combination for the vehicle stability test
remains a hot topic. There are some reports in this area.
In driving conditions, one of the key vehicle stability
controls is the accurate measurement of automobile state
parameters. This is also the premise and foundation for the
stability control system to control the vehicle [6]. But some
important vehicle state parameters either cannot be
measured through common sensors, or measuring cost is too
high. For example, a very important stability parameter for
vehicle control is the sideslip angle, which is the angle
between the direction of the longitudinal axis of the vehicle
speed and the direction of the vehicle body. It directly
affects the vehicle yaw moment which affects the stability of
the automobile. But unfortunately, at present no common
sensors can directly measure the vehicle sideslip angle and
the tire sideslip angle. The incomplete information of
vehicle stability control has caused great difficulties for the
implementation and promotion of the vehicle active safety
control system. Vehicle stability control system requires
estimating parameters such as adhesion coefficient of the
road, sideslip angle, and speed [7]. Vehicle sideslip angle
estimation algorithms are a common integral method, such
as the Kalman filtering method, fuzzy observer, Luenberger
observer, sliding mode observer and nonlinear observer.
Cho Y. [8] proposed a method which can estimate vehicle
sideslip angle based on an extended Kalman filter. The
vehicle speed estimation method is a comprehensive method
considering maximum wheel speed and road slope. Solmaz
S. [9] proposed a method of estimation based on rolling
horizon vehicle speed. Road adhesion coefficient has been
detected based on vehicle dynamics modeling and sensors.
Jun L. [10] proposed a road friction coefficient estimation
method based on an extended Kalman filter algorithm. Yang
Fuguang [11] proposed real-time pavement adhesion
coefficient estimation method based on the extended state
observer.
In vehicle active safety, Deng-Yuan Huang proposed a
feature-based vehicle flow analysis and measurement
system for a real-time traffic surveillance system [12] and
Jeng-Shyang Pan proposed a vision optical flow based
vehicle forward collision warning system for intelligent
vehicle highway applications [13]. The researchers make a
wide scope in vehicle active safety research.
Since the beginning of the 21 st Century, researchers have
been conducting research on the measuring stability of state
parameters of automobile. Yu Ming [14] from Southeast
University developed an automobile road five-wheel RTK
tester based on GPS carrier phase RTK technology. The
five-wheel tester can precisely measure vehicle motion
parameters and evaluate the vehicle movement performance
based on dynamic measurement. Xin Guan and his student
[15] from Jilin University have studied GPS/INS integrated
navigation algorithm for measuring vehicle state
information.
GPS is used for vehicle stability performance testing. It
can measure real-time vehicle stability parameters such as
running track, distance, azimuth, sideslip angle, speed and
acceleration. Differential GPS technology not only achieves
the online dynamic testing motion-state parameters, but also
brings the dynamic positioning precision to centimeter level.
For example, vehicle motion measurement speed precision
is up to 0.05 km/h, and sideslip angle accuracy is up to 0.15
degree for GPS and inertial navigation system of British
Oxford Technical Solutions company. Zhang Sumin [16]
used the inertia navigation system and GPS to estimate
vehicle speed, vehicle sideslip angle, yaw rate and other
status information. Kirstin L. Rock [17] from Stanford
University compared GPS/INS and auto optics test system
to verify the effectiveness of the GPS/INS to measure the
vehicle sideslip angle and speed.
David M. Bevly [18] from Auburn University estimated
three key vehicle parameters such as tire-slip ratio, sideslip
angle, and tire sideslip angle based on the GPS speed
measuring method. He integrated GPS speed sensor and
high frequency inertial measurement unit (low update rate
accelerometer), and provided an accurate estimation of
vehicle state parameters. In Canada, Automobile MultiSensors Research Center at the University of Calgary [19]
studied how to reduce the measurement errors and improve
testing precision. They proposed a navigation system with a
velocity update scheme that could predict and reduce the
error accumulation when there was a loss in GPS signals.
Ryu J. [20] from Stanford University proposed a method to
estimate the key parameters of vehicle stability based on
vehicle grade inertial sensors and GPS receiver. The method
can improve estimation accuracy for vehicle state
parameters under the influence of pitch and roll sensor bias
errors.
3. MODELS OF VEHICLE TESTING
A. Dynamical model of vehicle
In order to reflect an automobile motion state, this paper
establishes an eight degrees of freedom dynamic model
which includes vehicle rotary motion, longitudinal motion,
lateral motion, yaw motion, roll motion, four wheels rotary
motion, steering wheel angle, and vehicle speed. It assumes
that:
1. Automobile vertical and pitch motions are ignored;
2. Dynamic characteristics of four tires are the same;
3. The influence of air resistance is ignored;
4. The effect of sprung mass is ignored [21];
According to Fig.1., eight degrees of freedom dynamic
equations are presented as follows:
Longitudinal movement:
295
∑F
xi
∑F
xi
=m(v&x − v yγ )
=( Fx1 + Fx 2 )cos δ − ( Fy1 + Fy 2 )sin δ + Fx 3 + Fx 4
(1)
(2)
Lateral movement:
∑F
yi
=m(v& y + vxγ ) − ms hsϕ&&
Unauthenticated
Download Date | 9/19/17 10:59 AM
(3)
MEASUREMENT SCIENCE REVIEW, Volume 15, No. 6, 2015
∑F
yi
=( Fy1 + Fy 2 ) cos δ + ( Fx1 + Fx 2 )sin δ + Fy 3 + Fy 4
(4)
Kalman filter is a kind of linear filtering recursive
algorithm for discrete signal [19]. For a discrete system:
Yaw movement:
I zγ& + I xzϕ&& = ∑ M z
∑Mz = l f (Fy1 + Fy2)cosδ − (Fy3 + Fy4 )sinδ +
(5)
tf
(Fy1 − Fy2 )sinδ −
2
tr
(Fx1 − Fx2 )cosδ + l f (Fx1 + Fx2 )sinδ − (Fx3 − Fx4 )
2
2
(6)
tf
Roll movement:
I xφ&& − ms hs (v& y + vxγ ) + I xzγ& = ∑ M x
∑M
x
B. Model of a multi-stage Kalman filter
(7)
= −(kϕ f + kφ r )ϕ − (cφ f + cφr )ϕ& + ms ghs sin ϕ
(8)
(9)
lr
Fx 3
δ
α1
α3
y (k ) = H (k ) x(k ) + v(k )
(11)
Q(k ) n = k
E  w(n) wT (k )  = 
 0 n≠k
(12)
 R (k ) n = k
E v(n)vT ( k )  = 
 0 n≠k
(13)
E  w(n)vT (k )  = 0
(14)
Fx1
Fy 3
Fy1
vy
tr
γ
β
vx
Xv
α2
Where Q(k ) is noise covariance matrix, and R(k ) is
observation noise covariance matrix.
The first step of the Kalman filter is predicting the next
state of the system. If system state is x(k ) , then the next time
system state is:
tf
δ
α4
Fx 4
(10)
x(k ) is the system state vector. y (k ) is the system
observation vector. u (k ) is the system input vector. A(k ) is
the n × n system state matrix. H ( k ) is the m × n system
observation matrix. B(k ) is the 1 × n system input matrix.
w(k ) is the process noise vector. v(k ) is the observation
noise vector.
Assumptions and v(k ) were independent, and the noise is
normal distribution white noise, and v(k ) is expressed as
follows:
Four wheels motion equation:
I wiω& wi = Fxi Rw − Tbi ( i = 1, 2,3, 4 )
x(k ) = A(k ) x(k − 1) + B (k )u (k − 1) + w(k )
x(k + 1 k ) = Ax(k k ) + B (k )u ( k )
(15)
Fig.1. Eight DOF vehicle dynamic model.
The update status covariance matrix:
∑F
xi
are
wheels
longitudinal
resultant
P (k + 1 k ) = A(k ) P (k k ) AT (k ) + Q(k )
forces( i = 1, 2,3, 4 ). ∑ Fyi are wheels lateral resultant forces.
Z v is axis torque. m is vehicle mass and v y are velocity
components in X v and Yv . l f and lr are distances between
centroid to front and rear axle. t f and tr are distances
between front and rear wheel. γ and γ& are yaw velocity and
yaw angular acceleration. I x is the moment of inertia
around X v axle. I z is the moment of inertia around. I xz are
the moment of inertia around X v and Z v axle. ωwi are wheel
angular velocity( i = 1, 2,3, 4 ). I w i are wheel moment of
inertia( i = 1, 2,3, 4 ). Rw is wheel radius. Tbi are brake
torque( i = 1, 2,3, 4 ). δ is steering wheel angle. ms are vehicle
sprung mass. hs is the vertical distance from spring centroid
to the roll center. ϕ is side angle. ∆Fx ,eq and kϕ r are roll
stiffness of front and rear suspension. cϕ f and cϕ r are the roll
angle damping of front and rear suspension.
(16)
The state at k + 1 time is:
x(k + 1 k + 1) = x(k + 1 k ) + K g (k )[ y(k + 1) − H (k ) x(k + 1 k )] (17)
Where K g (k ) is Kalman gain:
K g (k ) =
P(k + 1 k ) H T (k )
H (k ) P(k + 1 k ) H T (k ) + R(k )
P (k + 1 k + 1) = [ I − K g (k ) H (k )]P (k + 1 k )
(18)
(19)
Kalman filter estimation algorithm has five formulas. If
giving initial value of state x(0) and state covariance
matrix P(0) , the system state can gradually be estimated by
the recursive method.
296
Unauthenticated
Download Date | 9/19/17 10:59 AM
MEASUREMENT SCIENCE REVIEW, Volume 15, No. 6, 2015
GPS and INS combination methods can be divided into
kinematic method and dynamic method. Kinematic method
is based on the motion relations of a car, and it does not rely
on the estimation of vehicle dynamics model. Because there
is no model error, measure accuracy depends on the
accuracy of testing device and the installation position, so
this method is very robust [16].
Vehicle sideslip angle fusion algorithm based on GPS/INS
is shown in Fig.2. The main parameters in GPS
measurement are heading angle ψ GPS , azimuth angle θGPS and
speed vGPS , and the main parameters of INS measurement are
yaw rate γ gyro , longitudinal acceleration ax , acc and lateral
ψ GPS is heading angle measured by GPS receiver. wψGPS is
GPS observation noise.
Yaw rate measured by gyroscope can be written as:
γ gyro = ψ& + γ ∆ + wγgyro
γ gyro is yaw rate measured by gyroscopes. ψ is heading
angle. γ ∆ is yaw velocity deviation. wγ is the gyro noise
(process noise).
The state equations of Kalman filter are written as follows:
gyro
ψ&  0 −1 ψ  1 
 w gyro 
x& =   = 
+   γ gyro +  γ 



γ&∆  0 0  γ ∆  0 
 0 
acceleration a y , acc .
vGPS
v x ,GPS = vGPS ⋅ cos (βGPS )
θ GPS
βGPS =θGPS −ψ
ψ GPS
(22)
Observation equation is written as:
v y ,GPS = vGPS ⋅ sin (βGPS )
ψ   wGPS 
y = ψ GPS = 1 0  +  ψ 
γ ∆   0 
ψ
γ gyro
a x , a cc
a
(21)
ψ   wψGPS 
or y = 0 0   + 

γ ∆   0 
y , acc
(23)
T
vx,vy
β = arctan
vy
vx ,
Fig.2. Sideslip angle combination algorithm block.
This work used a two-stage Kalman filter to fuse GPS and
INS measurements. First, yaw rate measured by gyroscope
and heading angle measured by double antenna GPS
receiver are fused by Kalman filter 1. The output is vehicle
course angle ψ . Longitudinal vx ,GPS and lateral v y ,GPS
velocities are calculated according to the azimuth angle θGPS
and velocity course angle ψ . Second, the longitudinal ax , acc
and lateral a y , acc acceleration measured by INS and the
longitudinal vx ,GPS and lateral velocities v y ,GPS are fused by
Kalman filter 2. The vehicle sideslip angle ratio can be
obtained according to the vehicle sideslip angle β .
Compared to the conventional GPS/INS algorithm, the
algorithm has some advantages: less state vector and
computing times. Thus, the algorithm can meet the
requirement for real-time vehicle stability control. When
GPS signal is lost, inertial navigation system can calculate
the vehicle sideslip angle. At the same time, inertial
navigation system achieves the error correction with GPS
information.
The state vector is ψ γ b  , and the input is the yaw
rate γ gyro measured by a gyroscope. The observation value is
the heading angle ψ GPS measured by GPS. If GPS is
available, the observation matrix C is [1 0]. If GPS is not
available, the observation matrix C is [0 0].
1. GPS measurement of the vehicle longitudinal and
lateral velocity
GPS measurement of the vehicle sideslip angle
βGPS =θGPS −ψ
(24)
βGPS is the sideslip angle measured by GPS and INS. θ GPS is
the azimuth measured by GPS. ψ is the heading angle
measured by GPS and INS.
GPS measurement of the vehicle longitudinal and lateral
velocity (vehicle body coordinate) can be written as:
vx,GPS = vGPS ⋅ cos( βGPS )
(25)
vy,GPS = vGPS ⋅ sin( βGPS )
(26)
If the main antenna of GPS is installed at the vehicle
centroid, longitudinal and lateral velocity can be written as:
vx ,GPS = vx + wxGPS
(27)
v y ,GPS = v y + wGPS
y
(28)
C. Vehicle stability parameters calculation
Heading angle measured by dual antenna GPS receiver can
be written as
ψ GPS = ψ + wψGPS
(20)
2. Longitudinal and
acceleration sensor
297
lateral
velocity
measured
Unauthenticated
Download Date | 9/19/17 10:59 AM
by
MEASUREMENT SCIENCE REVIEW, Volume 15, No. 6, 2015
ax , acc = v&x − ψ& ⋅ v y + a∆x + wax
(29)
a y , acc = v& y − ψ& ⋅ v y + a∆y + way
(30)
3. Vehicle sideslip angle calculation
Sideslip angle measured by GPS and INS is:
β = arctan
vGPS is speed measured by GPS. vx ,GPS and v y ,GPS are
longitudinal and lateral velocity components measured by
GPS. vx and v&x are longitudinal velocity and longitudinal
acceleration measured by sensor. v y and v& y are lateral
velocity, lateral acceleration measured by sensor. a x , ac c and
a y , acc are longitudinal, lateral acceleration measured by
acceleration sensor.
a ∆ y are longitudinal and lateral
GPS
acceleration deviations. wxGPS and wy are longitudinal and
lateral GPS receiver noise. wax and way are longitudinal and
lateral acceleration sensor noise.
Kalman filter state equation:
 v&x   0
&  
 a∆x   0
 v& y  =  −ψ&
  
&
 a∆y   0
−1 ψ&
0 0
0
0
0
0
0

0
−1

0 
 vx  1
  
 a∆x   0
 v y  + 0
  
 a∆y  0
0

0
1

0
 − wax 
 a x , acc   0 


+


 a y , acc   − way 
0


vx ,
When GPS signal is lost, no measurement can be done
for v x , GPS and v y ,GPS . But, a x , acc and ay, acc can be measured by an
INS sensor. Then the sideslip angle can be determined.
The sideslip angle measured by GPS is the sideslip angle
of GPS antenna. Usually the sideslip angle of vehicle
centroid and even the wheel sideslip angle are needed. As
the sideslip angle of GPS antenna is transformed into the
sideslip angle of any point at vehicle, there should be a
speed increment which angular velocity changes.
V p = V A + γ ⋅ RA / P
(34)
yaw rate.
The sideslip angle of the point P can be calculated by the
following formula.
Where ψ& = γ gyro − γ b
Kalman filter observation equation:
 (VP ) y 

 (V P ) x 
β p = tan −1 
 vx 
 
1 0 0 0  a∆x   wxGPS 
=
+  GPS 

0 0 1 0  v y   wy 
 a∆y 
(33)
V p is the speed at P point. VA is the speed at main antenna
of GPS. RA / P is the distance from main antenna to P. γ is
(31)
 v x , GPS 


v y ,GPS 
vy
(35)
(VP ) x and (VP ) y are the velocity components in the vehicle
(32)
body coordinates.
4. SIMULATION AND APPLICATION
vx
a∆x
vy
T
a∆y  is state vector, and vx , GPS
T
v y ,GPS  is
observation values.
In this work, vehicle structure parameters are used for
simulation shown in Table 1.
Table 1. Vehicle parameters table.
symbols
Meaning
Vehicle mass
value
1704.7
symbols
ms
Suspended mass
1526.9kg
kϕr
lf
Distance from centroid
to front axle
Distance from centroid
to rear axle
Distance between front
wheels
Distance between rear
wheels
Centroid height
1.035m
cϕf
1.655m
cϕr
1.535m
I wi
1.535m
Ix
Iz
m
lr
tf
tr
hc
Meaning
Front suspension
roll stiffness
Rear suspension roll
stiffness
Front suspension
roll damp
Rear suspension roll
damp
Wheel inertia
value
47298N·m/rad
Rw
Wheel radius
0.313m
0.542m
kf
55095N/rad
Roll inertia
744.0kg·m2
kr
Yaw inertia
3048.1kg·m2
A
Front wheel
cornering stiffness
Rear wheel
cornering stiffness
Front windward area
k ϕf
298
37311N·m/rad
2823(N·m)/(rad/s
)
2653(N·m)/(rad/s
)
0.99kg·m2
55095N/rad
1.8 m2
Unauthenticated
Download Date | 9/19/17 10:59 AM
MEASUREMENT SCIENCE REVIEW, Volume 15, No. 6, 2015
A. Simulation
The vehicle dynamics model is built using Carsim
software. The double lane change conditions are selected.
Double lane change simulation is more commonly used in a
vehicle stability testing, and it is a working state for the
simulation of vehicle overtaking and obstacle avoidance.
Fig.3. is a double lane change simulation route map. m,
B2 = 3.5B2 = 3.5 m, S1 = 60S1 = 60 m, S 2 = 40 S 2 = 40 m,
S3 = 60 S3 = 60 m. Then the steering wheel angle is shown
as Fig.4., the vehicle dynamic response is analyzed. Assume
that the speed is 120 km/h, the adhesion coefficients are 0.9
and 0.4, the parameters of simulation vehicle are shown in
Table 1., and the simulation results are shown as the
following Fig.5. to Fig.8.
Fig.6. Sideslip angle curve.
Fig.7. and Fig.8. are the curves at the adhesion coefficient
0.4. Because the vehicle is on the low adhesion road surface
and vehicle lateral force is the limit of lateral force, yaw rate
and sideslip angle greatly deviate from the ideal value, and
the vehicle is unstable.
Fig.7. Yaw rate curve.
Fig.3. Double lane change simulation.
Fig.8. Sideslip angle curve.
Fig.4. Steering wheel angle input curve.
Fig.5. and Fig.6. are yaw rate and sideslip angle curve,
respectively when the adhesion coefficient is 0.9.
The simulation experiment shows that the working
condition is very dangerous when vehicle loses its stability.
It is very difficult for a driver to keep vehicle stability when
vehicle loses its stability. So it is necessary to evaluate the
stability state of motion control system and other auxiliary
means for the automobile control.
B. Experimental apparatus
Fig.5. Yaw rate curve.
1. GPS device
This paper uses HV2 dual antenna dual function GPS
receiver. HV2 GPS receiver and antenna is shown in Fig.9.,
the receiver port and configuration is shown in Fig.10. and
Table 2. HV2 can provide accurate directions, and the GPS
positioning accuracy up to sub-meter level, 0.1 degrees
heading precision and 20 Hz data update rate.
299
Unauthenticated
Download Date | 9/19/17 10:59 AM
MEASUREMENT SCIENCE REVIEW, Volume 15, No. 6, 2015
Fig.12. VG800 INS sensors.
Fig.9. Crescent HV2 two antenna GPS receiver.
Table 3. Sensor connector setup.
Pin
A
B
E
F
G
H
J
K
L
M
N
P
T
U
W
Fig.10. GPS receiver port.
Table 2. GPS receiver port setup.
Pin
signal
description
2
TXD
3
RXD
5
ground
NMEA0183,binary, and RTC
input
NMEA0183,binary, and RTC
output
signal return
6
input
event input
2. Inertial navigation system experiment device
The system uses the VG800 INS sensor which is made in
MEMSIC Inc. The sensor terminal and the configuration are
shown in Fig.11. and Table 3., and the shape is shown in
Fig.12.
Signal
RS-422 Transmit Data
RS-232 Transmit Data
Positive Power Input (+)
Ground
Analog Output X-Accel Voltage
Analog Output Y-Accel Voltage
Analog Output Z-Accel Voltage
Analog Output Roll Rate
Analog Output Pitch Rate
Analog Output Yaw Rate
Analog Output Roll Angle
Analog Output Pitch Angle
Reserved – factory use only
System Fault
Reserved – factory use only
3. Oxford RT3102 inertial and GPS navigation system
An Oxford RT3102 inertial and GPS navigation system
instrument is used to verify to GPS and INS system
measurement. RT3102 instrument is shown in Fig.13. It is
Oxford Technical Solutions instrument for making precision
measurements of motion in real-time. It can measure the
vehicle longitudinal velocity, lateral velocity and sideslip
angle. RT3102 instrument technology parameters are shown
in Table 4.
Fig.11. Sensor connector.
Fig.13. Oxford RT3102 inertial and GPS navigation system.
300
Unauthenticated
Download Date | 9/19/17 10:59 AM
MEASUREMENT SCIENCE REVIEW, Volume 15, No. 6, 2015
Table 4. RT3102 technical parameters.
Position accuracy
Velocity accuracy
Heading
Acceleration Bias
Angular rate
Update rate
0.6 m CEP
0.1 km/h RMS
0.1°
10 mm/s2
0.01 °/s
100 Hz
4. Data acquisition device
In this paper an INDAS-5000 embedded system is the data
acquisition system which is shown in Fig.14. INDAS-5000
is on a printed circuit board (PCB) integrated real-time
embedded processor, field programmable gate array
(FPGA), and analog and digital I/O.
Fig.16. Heading angle curve.
Fig.14. Data acquisition system.
Fig.17. Course angle curve.
C. Measurement experiment
The single lane experimental conditions are selected to
measure the vehicle sideslip angle. Single lane experiment is
more commonly used in a vehicle stability testing. And it is
a working state for the simulation of vehicle overtaking and
obstacle avoidance. Fig.15. is a single lane experimental
route map. Similarly, high adhesion and low adhesion road
experiment should be carried out. m, S1 = 50S1 = 50 m,
S 2 = 30 S2 = 30 m.
B1
Fig.18. Sideslip angle curve.
Fig.15. Single change test road.
301
4.5
After Kalman filter
Before Kalman filter
4
Sideslip angle (degree)
Dual antenna receiver can directly measure the vehicle
sideslip angle. The dual antenna GPS receiver measures
vehicle centroid heading angle (Fig.16.) and the centroid
azimuth (Fig.17.), the difference between the heading angle
and the centroid azimuth is the sideslip angle (Fig.18.). The
vehicle sideslip angle measured by GPS/INS, which is fused
by a two-stage Kalman filter, is shown in Fig.19. From
Fig.19., it can be seen that the vehicle sideslip angle is
smooth after the Kalman filter algorithm. In Fig.20., the
vehicle sideslip angle, which is measured by 3102 sensor,
calibrates the sideslip angle measured by GPS/INS. The
curves are very similar.
3.5
3
2.5
2
1.5
1
0.5
0
2
4
6
8
10
12
Time (s)
Fig.19. Sideslip angle curve.
Unauthenticated
Download Date | 9/19/17 10:59 AM
14
MEASUREMENT SCIENCE REVIEW, Volume 15, No. 6, 2015
4.5
Sideslip angle (degree)
[2]
Measured by GPS/INS
Measured by RT3102
4
3.5
[3]
3
2.5
2
1.5
[4]
1
0.5
0
2
4
6
8
10
12
14
Time (s)
[5]
Fig.20. Sideslip angle curve measured by RT3102 sensor.
From the experimental data, it can be seen that the use of
vehicle dynamics model and Kalman filter algorithm,
especially the two-stage Kalman filter, is a good solution for
the problem of GPS signal loss and INS signal accumulation
error increase during vehicle stability testing. This method
meets well the real-time and accuracy requirements for
measurement of the vehicle stability key parameters.
[6]
[7]
[8]
5. CONCLUSION
Acquisition of vehicle driving state parameters, which
vehicle stability control required, is the premise and key
technology of the vehicle stability control. In response to the
need for vehicle stability critical state parameters testing, the
real-time and accuracy is the goal. In addition, adaptive
method, error compensation technology, statistical
characteristic and noise filtering are subjects in data fusion
for future research. This paper presents a robust method for
vehicle stability testing based on GPS/INS. Based on the
GPS velocity measurement technique, a robust method of
sideslip angle, speed and vehicle state parameters
measurement and estimation is proposed. Using the
integration of GPS and INS information, which are fused by
the two-stage Kalman filter, solves the problem of GPS
signal loss and low update rate. The RT3102 instrument is
used to verify the effect of GPS/INS measurement and
estimation of vehicle state parameters under typical driving
condition. The experimental results showed that the method
of GPS and INS measurement for vehicle stability key
parameters is accurate and real-time, and this method can
meet the test and design requirements of vehicle stability
controller.
ACKNOWLEDGMENT
[9]
[10]
[11]
[12]
[13]
[14]
The Department of Education of Heilongjiang and China
Association for Science and Technology provided the
financial support of this work. This work is supported by the
Research Projects under the Department of Education of
Heilongjiang Grant No.:12511453 and Overseas Research
and Development Program under Grant No.:10027.
[15]
[16]
REFERENCES
[1]
Yi, K., Chung, T., Kim, J., Yi, S. (2003). An
investigation into differential braking strategies for
vehicle stability control. Journal of Automobile
Engineering, 217 (12), 1081-1093.
[17]
302
Miao, Z.B., Zhang, H.T. (2013). Vehicle stability
testing using the dewetron test system. Electronics
World, 119 (1927), 24-26.
Sugiyama, M., Inoue, H., Uchida, K., Monzaki, S.,
Inagaki, S., Kido, S. (1997). Development of VSC
(vehicle stability control) system. TOYOTA Technical
Review, 46 (2), 61-68.
Nakazawa, M., Isobe, O., Takahashi, S., Wanatabe, Y.
(1995). Braking force distribution control for
improved vehicle dynamics and brake performance.
Vehicle System Dynamics, 24 (4-5), 413-426.
Wallner, E., Schiffmann, J. (2000). Development of an
automotive rollover sensor. SAE Technical Paper
2000-01-1651.
Yoon, J., Yi, K. (2006). A rollover mitigation control
scheme based on rollover index. In American Control
Conference 2006, 14-16 June 2006. IEEE, 163-179.
Yoon, J., Cho, W., Koo, B., Yi, K. (2009). Unified
chassis control for rollover prevention and lateral
stability. IEEE Transactions on Vehicular Technology,
58 (2), 596-609.
Cho, Y., Kwak, B. (2007). A control and analysis of
vehicle rollover based on electronic stability control.
SAE Technical Paper 2007-01-3566.
Solmaz, S., Corless, M., Shorten, R. (2007). A
methodology for the design of robust rollover
prevention controllers for automotive vehicles with
active steering. International Journal of Control, 80
(11), 1763-1779.
Jun, L. (2006). Research and simulation of vehicle
stability control strategy. Thesis, Wuhan University of
Science and Technology, China.
Huang, D.Y., Chen, C.H., Hu, W.C., Yi, S.C., Lin,
Y.F. (2012). Feature-based vehicle flow analysis and
measurement for a real-time traffic surveillance
system. Journal of Information Hiding and Multimedia
Signal Processing, 3 (3), 282-296.
Pan, J.S., Ma, S., Chen, S.H., Yang, C.S. (2015).
Vision-based vehicle forward collision warning system
using optical flow algorithm. Journal of Information
Hiding and Multimedia Signal Processing, 6 (5),
1029-1042.
Yang, F., Li, Y., Ku, H., Rong, X., Song, R. (2010).
Attached to real-time estimation of coefficient of
extended state observer based on the pavement.
Journal of Agricultural Machinery, 41 (8), 6-9.
Yu, M., Qian, L. (2005). Vehicle movement
measurement based on RTK five wheels. Automotive
Engineering, 27 (1), 54-56.
Zhang, S. (2006) Research on algorithm for vehicle
state testing system based on INS and GPS. Thesis,
Jilin University, China.
Jiang, H. (2008). Dynamic performance test of
automobile based on GPS and gyro. Thesis, Xihua
University, China.
Rock, K.L., Beiker, S.A., Laws, S., Gerdes, J.C.
(2005). Validating GPS based measurements for
vehicle control. In ASME 2005 : Dynamic Systems and
Control Division, 5-11 November 2005. ASME, 583592.
Unauthenticated
Download Date | 9/19/17 10:59 AM
MEASUREMENT SCIENCE REVIEW, Volume 15, No. 6, 2015
[18] Bevly, D.M., Gerdes, J.C., Wilson, C., Zhang, G.
(2000). Use of GPS based velocity measurements for
improved vehicle state estimate. In American Control
Conference, 28-30 June 2000. IEEE, Vol. 4, 25382542.
[19] Chung, T., Yi, K. (2006). Design and evaluation of
side slip angle-based vehicle stability control scheme
on a virtual test track. IEEE Transactions on Control
Systems Technology, 14 (2), 224-234.
[20] Ryn, J. (2004). State and parameter estimation for
vehicle dynamic control using GPS. Thesis, Stanford
University, CA.
[21] Zhang, J.Z. (2012). Study on vehicle stability control
system based on GPS. Thesis, Harbin Engineering
University, China.
303
Received February 10, 2015.
Accepted December 1, 2015.
Unauthenticated
Download Date | 9/19/17 10:59 AM
Was this manual useful for you? yes no
Thank you for your participation!

* Your assessment is very important for improving the work of artificial intelligence, which forms the content of this project

Download PDF

advertisement