Research of the vehicle with AFS control strategy based on fuzzy logic

International Journal of Research in Engineering and Science (IJRES)
ISSN (Online): 2320-9364, ISSN (Print): 2320-9356
www.ijres.org Volume 3 Issue 6 ǁ June 2015 ǁ PP.29-34
www.ijres.org 29 | Page
Research of the vehicle with AFS control strategy based on fuzzy
logic
Wang Yunchao1
，Chen Hao2,
Li Hui，Lu Changyu
1
(College of Automotive Engineering，Shanghai University of Engineering Science，China)
2
(College of Automotive Engineering，Shanghai University of Engineering Science，China)
ABSTRACT : Vehicle stability is an important index for automobile safety. With the rapid development of auto
industry, more attention was emphasized on active front wheel steering technology. A wheel angle control
strategy of active front wheel steering was present in this paper. Research was conducted based on Carsim and
Simulink. Carsim software was used to establish whole vehicle model. The fuzzy logic angle controller was
designed based on MATLAB/Simulink. Yaw rate error and error rate of change were the input variables of the
controller, and additional angle of front wheel was the output variable to validate the simulation. Through the
analysis of simulation results, it was confirmed that the control method can control the Yaw rate of the vehicle
effectively, which improved the stability of vehicle.
Keywords - Active front steering; Yaw rate; Fuzzy control; Stability; Simulation
I. INTRODUCTION
With the rapid development of auto industry, all kinds of high and new technology constantly emerging,
higher quality demand of vehicle is getting more and more important for consumers.As an essential component
of an automotive brake system and transmission system, steering system plays an important role in the safety in
the process of driving. Its performance directly affects the vehicle steering stability and safety comfort, etc.
Active front steering system is the effective combination of the reliability and flexibility. Its improvements in
the larger development space for the automotive steering performance. The core of the active front steering
technology is through to the additional angle of front wheel on a not dependent on the driver's steering wheel
input to improve the performance of vehicle handling stability and keep track[1]
.According to the different way
of additional angle overlay, and it can be divided into mechanical and electronic type[2]
.
In order to maintain the safety of the vehicle handling characteristics, people need to control the Yaw rate of
the vehicle. So there are two main methods to control. One of them is the DYC, it by using different longitudinal
force side of the car, to make the car can generate external stability Yaw rate of the vehicle. Another is by
controlling the front wheel steering angle of the vehicle. The most practical and most effective steering angle
control method is the active front steering system, AFS. This method is the driver on the basis of the existing
steering input added a superposition of the steering angle cor [3]
.
This paper presents a wheel angle control strategy of active front wheel steering. This strategy is by
adjusting the front wheel angle actively to correct the Yaw rate of the vehicle. And then it can realize the
stability control of the vehicle. According to the fuzzy control theory, using Simulink simulation platform, Yaw
rate as control variables to make corresponding control strategy. And doing the simulation study in double lane
change conditions is that the vehicle model with a fuzzy controller and without it in vehicle dynamics software,
Carsim. By analyzing the simulation results, it can be confirmed that the control method can control the
vehicle’s Yaw rate effectively and improve the stability of the vehicle.
II. ARCHITECTURE DESCRIPTION
In this paper, the Architecture Description diagram is shown in Fig.1.The control system was mainly
composed of two module, Carsim vehicle model and AFS controller. Carsim outputs steering wheel angle,
velocity and vehicle Yaw rate to AFS controller by car in running condition. Then they through the control
strategy of the operation of the controller output vehicles’ additional steering angle cor of front wheel, and they
feedback to Carsim vehicle model. The system corrects the Yaw rate of vehicle completely by circle. That means
the vehicle stability control.

Research of the vehicle with AFS control strategy based on fuzzy logic
Carsim model
AFS
controller
Monitor
Wheel angle
scope
No
signal
Steering Angle
Vx
Yaw
Rate
Active Front Steering
Wheel Angle
AFS Enable
AFS Disable
Fig.1 architecture description diagram
III. AFS CONTROL SYSTEM DESIGN
The Yaw rate and the side-slip angle of vehicle are important control parameters of the stability of the auto
control system. But the side-slip angle can not be measured directly. And in most cases it is usually calculated
indirectly by the method of estimation. Yet Yaw rate of the vehicle is obtained by Yaw rate sensor, so the Yaw
rate is quite exact parameter[4]
.Therefore, when the controller is designed, putting the error e and error rate of
change ce between the actual Yaw rate r and theory Yaw rate dr as the input of the controller, and the output
variables u provide additional angle cor of the wheel. As shown in Fig. 2 was the AFS control strategy.
Fig.2 AFS control strategy

3.1 The Theory Arithmetic of Yaw rate
As shown in Fig.2, the Bicycle Model module is the theory arithmetic of Yaw rate. It is a model of a two-
degree of freedom. Through the input of velocity Vx（km/h）and steering wheel angle（deg）, method of logic
threshold was used, to calculate the theory arithmetic of Yaw rate（rad/s）,and its computation formula was as
follows:
2
driverxd / xukl   (1)
Fig.2 also showed the error of Yaw rate. It is defined as:
 -e d (2)
Among them, is the actual Yaw rate, is the theory Yaw rate.
3.2 Fuzzy Logic Controller Design
Fuzzy control can adapt to variable nonlinear system control well, and it has strong robustness[5]
.For the
characteristics of the front wheel active steering control, the fuzzy controller is designed according to the error
eand error rate of change ce of the vehicle Yaw rate as the input signal of fuzzy system. The output signal is
additional steering angle cor of front wheel. Then the fuzzy controller was designed. With fuzzy controller of
the system block diagram was shown in Fig. 3.
Fuzzification
Fuzzy
Control
Rulesde/dt
rd-
r
+
E
EC
U Car
Model
Defuzzification
Fig.3 fuzzy controller system block diagram
According to the size of the error between the actual Yaw rate and the theory Yaw rate of the output of the
reference model car, quantificational field of fuzzy variable E,EC,U have been established[6]
.Defines as follows:
The quantificational field E：{-6，-5，-4，-3，-2，-1，0，1，2，3，4，5，6}
The quantificational field EC：{-1，-0.8，-0.6，-0.4，-0.2，0，0.2，0.4，0.6，0.8，1}
The quantificational field U：{-6，-5，-4，-3，-2，-1，0，1，2，3，4，5，6}
In each domain, the fuzzy subset of membership function was defined as seven language variables
respectively，PB(Positive Big)，PM(Positive Medium)，PS(Positive Small)，ZE(Zero Error)，NS(Negative
Small)，NM(Negative Medium)，NB(Negative Big).The language variables of the input and output adopt
gauss membership function. As shown in figure 4 to 6.
Fig.4 The membership function of input variable E
 d

Fig.5 The membership function of input variable EC
Fig.6 The membership function of output variable U
In order to make the fundamental field of the actual variable error e, error rate of change ec, controlled
quantity u correspond the field of the after standardization variable error E, error rate of change EC, controlled
quantity U, it can be concluded that the following quantitative factor according to the range of field:
Quantitative factors of e： ek =2
Quantitative factors of ec： eck =10
Quantitative factors of u： uk =15
Considering error was negative. When the error was NB, no matter what the error rate of change was. In order
to eliminate deviation, it should increase the controlled quantity. So controlled quantity should be PB. When the
error was NS or Zero, the main contradiction is converted to the stability problem of the system. In order to
prevent overshoot amount is too large, and make the system stable as soon as possible, it will be determined the
change of controlled quantity according to the error rate of change. When the error rate was negative, the
deviation had a tendency to increase. At this time it should increase the controlled quantity to prevent further
deviation[7]
.According to the analysis, fuzzy control rule table can be got, and it produced 49 fuzzy rules in total.
In this paper, fuzzy control approach was “Mamdani”.Reasoning method was “max-min”. The method of
defuzzification was the center of gravity.
IV. SYSTEM SIMULATION AND EXPERIMENTALANALYSIS
In this paper, the use of Simulink modules in MATLAB software was used to set up simulation model of
vehicle stability control system. The fuzzy control toolbox was in the software MATLAB. Using it to establish
fuzzy control rules and membership functions and putting it into Simulink. In order to make the vehicle more in
line with the actual condition of the vehicle, whole vehicle model was established in the software Carsim.
Simulation was conducted based on Carsim and Simulink [8][9]
.
This paper introduced the double lanes change experiment on the vehicle dynamics simulation test platform
Carsim. In the test, the conditions were set as follows, Road adhesion coefficient was 0.85, velocity was
50km/h, simulation time was 8s.Vehicle model selection was D-Class SUV, in which one was with AFS
controller, and the other one was not with AFS. As shown in Fig 7-10 were double lane change experimental
results of the vehicle.

Fig.7 Yaw rate response curve
Fig.8 Lateral acceleration response curve
Fig.9 Target trajectory tracking

Fig.10 The front wheel steering Angle curve
Fig.7 and Fig.8 were Yaw rate response curve and lateral acceleration response curve. It can be seen from
the figures in red curve that was the vehicle equipped with the controller. Comparing with the blue curve, it has
obvious improvement. At around 4.5s, the vehicle's lateral parameter began to be controlled. AFS controller
played an important role in it. Controller made the Yaw rate error recovery gradually. Close to 5.5s, equipped
with the controller of the vehicle’s lateral parameter is the convergence, and shortening response time. The trend
was to restore to a stable state in the end. It was indicated that the vehicle had good stability with control.
Fig.9 was target trajectory tracking. It can be seen from the diagram that the car had better tracking
performance with the stability control. Compared with no controller vehicles, its running wide range was
narrow. It can be a relatively good in accordance with the driver's intention.
Fig.10 was the front wheel steering angle curve. Within 3.5s to 4.5s, wheel steering angle rotated in
advance due to the restrain of AFS controller. It reduced the yaw rate error of the vehicle in both left and right
wheels. While without controller, steering angle were up to the maximum, which induced to bad tracking. After
about 5s, AFS wheel base in a steady state. But it can be seen from the curve of serrated, the front wheel steering
angle continues to correct in order to make the stability control of the car.
V. CONCLUSION
This paper used fuzzy controller design of active front wheel steering system. Its purpose was to improve
steering stability of the vehicle. As it can be seen from the simulation results, the Yaw rate and the lateral
acceleration curve peak of the vehicle with a fuzzy controller were smaller than that without control, and also
stable time was relatively short. It can been seen its security and stability of vehicle control system. Through the
software Carsim under the condition double lane change of test, the simulation data show the effectiveness of
the fuzzy control strategy is proposed in this paper. It had a certain positive role in improving the traffic safety.
VI. Acknowlegement
This work was financed by the National Natural Science Foundation of China (Grant No. 51305252) and
Shanghai Education Committee Project(13YZ109).
REFERENCE
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[3] Wen Xiaonan,Chai Weihong. AFS/DYC Integrated Control Strategy Research Based on Fuzzy Logic[J]. Mechanical
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[4] Li Hongliang,Sun Renyun,Yao Shiyin. Vehicle Stability Control Research Based on Fuzzy Technology[J]. Light vehicle
technology,2008,03:8-11.
[5] Ma Chunhui. The Vehicle ESP System Control Model and Method Research Based on MATLAB[D]. Nanjing University of Science
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[6] Xu Zhe,Wei Minxiang. Design of Vehicle Stability Control System Based on Fuzzy Logic[J]. Mechanical Science and
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[7] Zhao Wei,Wei lang,Zhou Zhili,Zhang Wei. Study on Vehicle Braking Stability Control Based on Active Steering Technique[J].
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