Design and HIL Verification of Fuzzy Identification Controller for Electronic Parking Brake System Based on dSPACE Haotian Wu*, Chengcai Zhang, Yongjie Tong, Xuexun Guo
Technology Center
School of Automobile Engineering
Wanxiang Qianchao CO., LTD.
Wuhan University of Technology
Hangzhou, China
Jie Zhang
Wuhan, China *
[email protected]
Abstract—In recent years, there are many studies on controller
dSPACE systems are flexible in HIL simulation because
of electronic parking brake (EPB). These studies develop EPB
they play a bridge between the MATLAB/Simulink model and
controller based on vehicle dynamic equation. However, few
physical hardware, such as sensor, actuator and controller so
studies focus on the identification of driving intention and
on. dSPACE real time interface (RTI) blocks are easily
running condition. This paper extends the method to identify
accessed by MATLAB command. All kind of analog and
target value of parking brake during hill-start and designs a
digital signals can also be monitored by using graphical user
fuzzy identification controller (FIC) according to drivers’
interfaces (GUI) designed with ControlDesk software
experience. Finally, we verify the FIC’s performance by
These GUIs shorten the design period and can make control
hardware in the loop simulation based on dSPACE platform.
parameters visualized, so the HIL verification of FIC is more
The testing results indicate that the controller is suitable for
available and creditable based on dSPACE platform [4].
identification of driving intention and running condition. Keywords-Real Time Interface, Hardware in the Loop Simulation, Fuzzy Control, Electronic Parking Brake.
[3]
.
In this paper, we use HIL to verify whether the fuzzy identification controller (FIC) for electronic parking brake (EPB) system can correctly recognize the hill slope and driver intension. EPB systems frequently have an inclination sensor
I. INTRODUCTION
integrated into the ECU, which prevents the vehicle from
The history of hardware-in-the-loop (HIL) simulation in [1]
rolling backwards when the vehicle starts on a slope. Besides,
automotive engineering goes back to the 1980s . The first
EPB systems need throttle sensor and clutch sensor to identify
HIL test systems for ABS ECUs were built at the end of the
vehicle running condition. In our study, there are three parts.
[2]
1980s . After that the spread of HIL is rapid.HIL simulation
The first part shows that how HIL simulation hardware system
is more reliable than totally virtual simulation, so this method
is designed. The second part will detail how a FIC is designed
is more popular than before and is used in control strategy
in fuzzy toolbox of MATLAB and how the parameters are
validation (CSV) or controller function verification (CFV).
defined based on driving experience. In the end, the HIL
The CFV includes two types: The former is that the Controller
simulation experiments are taken. The results show whether
is a real hardware but the signal is a virtual one which is based
FIC could effectively identify the target parking brake torque
on mathematical model. The latter is that the signal is real but
by comparing with theoretical value.
the controller is based on math model. Both modes are widely
II. HARDWARE SYSTEM DESIGN
used in controller development and validation. This study makes use of the second mode. The FIC is developed in MATLAB /Simulink, and Simulink model build physical connections by dSPACE platform.
978-1-4244-8039-5/11/$26.00 ©2011 IEEE
A.
Hardware System Structure The EPB is a system which controls the motor to pull the
parking cable during vehicle parking and hill-start
[5]
. EPB
system doesn’t only ensure the vehicle parks safely, but also
The signal to control ISP sub-system is generated by
assists hill-start. So before design an actuator controller, it is
dSPACE digital pulse width modulation module, whose digital
foremost to design a target brake torque identification
value range from 0 to 655536.
controller. Usually, EPB Controller should identify the target brake torque based on precise mathematical equation. In this paper a FIC is developed and is verified by HIL simulation. FIC infers the target brake torque according to inclination sensor, throttle sensor and clutch senor. Fig.1 shows us the
Besides, in order to ensure the target inclination angle can be gotten, a controller is designed based on position feedback. The design of ISP controller isn’t detailed in this paper; in another paper, this problem will be illuminated.
system structure of HIL simulation. In the hardware system, the signals of clutch, throttle and two-axis accelerometer sensor are modulated by signal unit and then accessed to DS2210 I/O board. Afterwards, processor board of AutoBox samples the signals then transfers data into PC by Ethernet. Besides, power unit should be used because the I/O drive capability is too limited to drive the DC motor in inclination simulator platform (ISP).
Figure.2 G63$&( $872%2;
Structure of ISP sub-system
The ISP system should follow target inclination angle 3&
precisely and quickly. Thus fast response and little steady-state error must be satisfied. Fig.3 shows the step response
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performance of ISP sub-system, whose settling time is less than 35 milliseconds and overshooting is less than 12%, which are available for HIL simulation.
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B.
HIL hardware system structure
ISP Sub-system The accurate simulation of road inclination is the
foundation to verify the hill-start function of controller. Hence, the development of inclination simulator platform (ISP) is first important for HIL hardware system. In our study, screw and Figure.3
nut mechanism, two DC motors and gearboxes comprise executive body of ISP just as Fig.2 shows.
ISP sub-system step response
C. Throttle and Clutch Sub-system
The platform is a plastic board with mounting holes for
During the verification experiment, an experienced driver
testing ECU board. Two DC motors respectively drive two sets
will maneuver accelerate paddle and clutch paddle just like
of screw and nut mechanism to make the platform rotate in
driving process in real scenario. Therefore, real clutch and
transverse and longitudinal direction. Longitudinal direction
throttle sensors are used in hardware system, when we do
rotation simulates the inclination angle of road. Transverse
CFV.
rotation simulates the passenger disturbance to mass estimation, which will be studied in near future.
Moreover, in order to calculate theoretical value, the throttle-engine model and clutch-torque model should be established
precisely
[6]
.
And
then
the
mathematical
relationship between signal value and torque is identified. In
In addition, K1 and K2 are the gains to convert input
this study, we did not develop precise model but we obtained
variables to fuzzy quantity. K3 and K4 are the gains to turn the
the curve by experiment. Fig.4 is the Torque-Signal
output into real control value. BRK represents output of fuzzy
characteristic curve of engine.
inference system. The development of FIC normally includes the following steps [8]: z Turning input and out variables into fuzzy quantity; z Defining membership function and fuzzy rules; z Building fuzzy inference mechanism; z Defuzzificaion of output set;
Figure.4
In the following, the development of FIC will be
Engine torque map
Those curves will be used to calculate the precise
illuminated step by step. First, analog signal from sensor
theoretical brake torque, but not be used by FIC. We just make
should be digitalized, and then classified into several grades.
use of those curves to establish fuzzy rules during development
The value of input variables is obtained from ADC block,
of FIC, and then FIC directly samples the signals of accelerate
whose resolution is 12bits. Generally, voltage range of sensors
pedal and clutch pedal sensors to infer drivers’ intention and
is from 0V to 5V.However, in order to reject disturbance, ceil
running condition.
threshold and floor threshold should be set, so in our study signal output ranges from 0.5-4.5V. Digital value should be
III. FUZZY IDENTIFICATION CONTROLLER
calculated by
Fuzzy control system is based on fuzzy set which can contain elements with a partial degree membership and
vd
without clearly defined boundary, so it is suitable to solve the problem, which has no precise math model but with rich expertise system or linguistic experience [7].
ui u 2 Nbit uref
Where vd denotes digital value, Nbit is the resolution of ADC, ui and uref respectively denote sampled signal voltage
Fig.5 is the diagram of the fuzzy identification controller
and ADC reference voltage, 5V in this study. According to this
for EPB. It has three input variables: throttle (THR), clutch
equation, we get the value range is from 102 to 918. For easy
(CLU) and inclination (INC). Three fuzzy variables make the
calculation, we can re-scale the range and make all input
inference system too complicated to calculate, so it is better to
variables in the same domain, [1, 9]. The range of output is
simplify the fuzzy variables. In this paper, we use throttle and
from 0 to 10.
clutch sensor as fuzzy input and inclination as an output weight.
For throttle, clutch and brake, corresponding variables are divided into 5 grades: ZE (Zero), SM (Small), ME (Medium), BG (Big), and VB (Very Big). For throttle, ZE represents minimum throttle opening. For clutch, VB represents
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disengaged status. Next step is to define membership functions .
3 5 2 ' 8 7
(MF) associated with THR, CLU and BRK. Gaussian MFs are 2XW
used for two inputs due to its high precision and smoothness. The Gaussian membership function is defined by
.
f ( x;V , c )
( x c )2
e
2V
2
The fuzzy system usually truncates and aggregates all outputs associated with each rule, so the calculation is very Figure.5
Fuzzy identification controller for EPB
complicate, so the simplest membership function is better for BRK. Its MF is defined by
f ( x; a, b, c)
§ § xa cx· · max ¨ min ¨ , ¸,0¸ © ba cb ¹ ¹ ©
All MFs associated with each variable are showed in Fig.6.
looking up data. Through experiment, we found that throttle position and cultch position are key factors to judge the work status of parking brake and inclination is more relevant to the amount of braking force. Hence, the FIC controller can be simplified to the model as Fig.5 shows. Tab.1 shows the fuzzy rules for the simplified model. TABLEI.
(a) MFs for THR
FUZZY INFERENCE RULES
CLU THR
VB
BG
MD
SM
ZE
ZE
VB
VB
VB
VB
VB
SM
VB
VB
VB
BG
BG
MD
VB
VB
MD
MD
SM
BG
VB
BG
SM
SM
SM
VB
VB
BG
SM
ZE
ZE
Based on this rules set, inference system will calculate the output of each rule and then aggregate all output by maximum function. Finally, the fuzzy output is defuzzified by [10] n
¦m r
i i
r
(b) MFs for THR
i 1
mi
Where mi is the mass of the ith element and ri is the corresponding coordinates. IV. FIC VERIFICATION EXPERIMENT After completing the fuzzy identification controller, we must firstly evaluate how well the FIC’s output follows the experts’ experience by simulation, and then put the FIC in front of actual users to validate FIC performance. Fig.7 shows that FIC’s output in the clutch coupling region is smaller than
(c) MFs for BRK Figure.6
the output in clutch disengaging and small throttle closed area,
MFs for input and ouput variables
In the following step, the rules set will be established
which is, in some way, meet experts’ experience.
according to the drivers’ experience. In inference system, the number of input variables and grades for each variable determines the amount of rules. The number of rules can be calculated by [9] n
R
r
i
i 1
Where ri is the quantity of grades for each variable, and n is the number of variables. In this study there are 3 input variables and 5 grades for each variable, so there are 125
Figure.7
Engine Torque Map
if-then rules. In real time system, three input variables must be
In the following, we focus on the verification of FIC in
stored in three dimension array, which leads to difficulty in
the real scenario. In the experiment, real acceleration sensor
V. CONCLUSION AND FUTURE WORK
and throttle position sensor and clutch position sensor will be connected to ADC modular of DS2210, an input and output
Previous studies have focused on the development of
(I/O) board. Then the MATLAB/Simulink program is
actuator controller for electronic parking brake system (EPB).
downloaded into the real-time hardware of AutoBox, processor
However, little attention was paid into the research on
board DS1105. Fig.1 shows how the hardware configuration is
identification controller for driving intention and running
like.
condition. This study develops an identification controller the
based on fuzzy control theory. In addition, we verify the
acceleration paddle and clutch paddle like real driving process.
performance of fuzzy identification controller (FIC) by HIL
Afterwards, the dSPACE real-time system samples all input
simulation based on dSPACE platform. The HIL simulation
signals and calculates the output based on fuzzy identification
shows that this controller is suitable for identification of
controller program in processor board. In the end, we compare
driving intention and running condition during hill-start
the brake torque value inferred by fuzzy inference with the
process.
Furthermore,
an
experienced
driver
operates
precise theoretical value to identify how well the FIC works.
It should be noted that this study has only examined
Fig.8 shows this entire process of hardware in the loop
whether FIC’s output can follow the theoretical value. In near
simulation.
future, we will consider how well the controller works in real cars. The evaluation work will be based on flexible Micro AutoBox. REFERENCES [1]
Figure.8
HIL simulation with real time interface
During an experiment cycle, due to different inclination angles, the driver makes some action preparation for 3 seconds, completes the hill-start process within 6 seconds and then holds acceleration paddle and frees the clutch paddle for 2 seconds. The same experiment is taken three times. Finally, the difference between the average experimental value and theoretical value is calculated. Fig.9 shows that FIC response can match the theoretical value well. Even though there is less than 5% error near coupling point, before the coupling point FIC’s output is bigger than theoretical one. So the rear slide also can be avoided.
Figure.9
Difference between Theoretical Value and Controller Output
Gehring.J, Schutte.H. Automated Test of ECUs in a Hardware-in-the-Loop Test Bench for the Validation of Complex ECU Networks. Society of Automotive Engineers, SAE Paper No. 2002-01-0801. [2] Bach.Th, Real-time Simulation in Anti-lock Brake System Development based on a Personal Computer. VDI 6th International Congress "Measurement and Testing Techniques in the Automotive Industry", Berlin, April 28, 1992. [3] dSPACE ControlDesk, Hardware product information http://www.dspaceinc.com [4] A. Dhaliwal, S. C. Nagaraj, S. Ali, “Hardware in the loop Simulation for Hybrid Electric Vehicles-An Overview, Lessons Learned and Solutions Implemented”, SAE Technical Paper Series 09AE-0198 [5] Young O. Lee, Choong W. Lee, Chung C. Chung, Youngsup Son, Paljoo Yoo, Inyong Hwang. Stability Analysis of an Electric Parking Brake (EPB) Systemwith a Nonlinear Proportional Controller. Proceedings of the 17th World Congress, the International Federation of Automatic Control Seoul, Korea, July 6-11, 2008. [6] Yi-qiang PENG, Wei LI.Research on Fuzzy Control Strategies for Automotive EPB System with AMESim/Simulink Co-simulation. Chinese Control and Decision Conference (CCDC 2009), 2009. [7] PENG Yiqian, LI Wei. Identification and Modeling Parking Brake System of Automotive Electrical for SiL Simulation. Journal of Southwest Jiaotong University (English Edition). Vol.16, No.4, Oct.2008. [8] Han-Xiong Li, Lei Zhang, Kai-Yuan Cai, Guanrong Chen. An Improved Robust Fuzzy-PID Controller with Optimal Fuzzy Reasoning. IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS—PART B: CYBERNETICS, VOL. 35, NO. 6, DECEMBER 2005. [9] H.X.Li, H.B.Gatland.“Conventional fuzzy control and its enhancement,” IEEE Trans. Syst., Man, Cybern. B, vol. 26, no. 5, pp. 791–797, Oct.1996. [10] L. Zhang and K. Y. Cai, “A new fuzzy reasoning method,” Fuzzy Syst. Math., vol. 16, no. 3, pp. 1–5, 2002.
