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浙江大学学报(工学版)
Journal of ZheJiang University (Engineering Science)  2025, Vol. 59 Issue (9): 1942-1953    DOI: 10.3785/j.issn.1008-973X.2025.09.018
    
Vehicle’s four-wheel steering and direct yaw moment control considering driving styles
Shu WANG(),Haichuan ZHANG,Cangyan GUO,Xuan ZHAO*(),Huixin GUO
School of Automobile, Chang’an University, Xi’an 710000, China
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Abstract  

A hierarchical architecture coordinated control strategy considering driving styles for active four-wheel steering (AFWS) and direct yaw moment control (DYC) systems was proposed to improve the handling stability of distributed drive electric vehicles and accommodate the driving styles of different drivers. This strategy employed a three-layer control architecture, including the upper controller, the middle controller, and the lower controller. A reference model for handling stability considering driving styles was established in the upper controller. The stability factors of vehicles with different driving styles were determined through driver-in-the-loop experiments, and the vehicle states were categorized into stable, transitional, and unstable regions based on the phase plane theory. A hybrid game control model for AFWS and DYC based on Stackelberg leader-follower game and Pareto cooperative game was established in the middle controller to improve the vehicle’s handling stability under complex driving conditions. The lower controller was used to optimize the wheel drive torque distribution with the goal of minimizing the tire load rate. The driver-in-the-loop test platform was built based on the Simulink simulation software and the Logitech G29 driving simulator, and open-loop and in-loop tests with drivers were conducted. The results indicated that the proposed control strategy can adapt to the driving styles of different drivers and meet their personalized needs, thereby improving the vehicle’s handling stability.

Key words： distributed drive electric vehicle      driving style      active four-wheel steering      direct yaw moment      phase plane     
Received: 12 October 2024      Published: 25 August 2025
CLC:  U 46  
Fund:  国家自然科学基金资助项目（52472397,52372375）；陕西省重点研发计划资助项目（2024GX-YBXM-260）；陕西省科技成果转化计划资助项目（2024CG-CGZH-19）.
Corresponding Authors: Xuan ZHAO     E-mail: shuwang@chd.edu.cn;zhaoxuan@chd.edu.cn
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Shu WANG
Haichuan ZHANG
Cangyan GUO
Xuan ZHAO
Huixin GUO
Cite this article:

Shu WANG,Haichuan ZHANG,Cangyan GUO,Xuan ZHAO,Huixin GUO. Vehicle’s four-wheel steering and direct yaw moment control considering driving styles. Journal of ZheJiang University (Engineering Science), 2025, 59(9): 1942-1953.

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https://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2025.09.018     OR     https://www.zjujournals.com/eng/Y2025/V59/I9/1942


考虑驾驶风格的车辆四轮转向和直接横摆力矩控制

为了改善分布式驱动电动汽车的操纵稳定性，并考虑不同驾驶人的驾驶风格，针对主动四轮转向（AFWS）和直接横摆力矩控制（DYC）系统，提出基于分层架构的考虑驾驶风格的协调控制策略，包含上、中、下3层控制器. 在上层控制器建立考虑驾驶风格的操纵稳定性参考模型，通过驾驶人在环试验确定不同驾驶风格的车辆稳定性因数，并依据相平面理论将车辆工作区间划分为稳定域、过渡域与失稳域；在中层控制器建立基于Stackelberg主从博弈和Pareto合作博弈的AFWS和DYC混合博弈控制模型，提高车辆在复杂行驶工况下的操纵稳定性；在下层控制器以轮胎负荷率最小化为目标，优化车轮驱动转矩分配. 利用Simulink仿真软件和罗技G29驾驶模拟器搭建驾驶人在环试验平台，进行驾驶人开环和在环试验，结果表明，提出的控制策略能够适应不同驾驶人的驾驶风格，满足其个性化需求，从而提高了车辆的操纵稳定性.


关键词： 分布式驱动电动汽车,  驾驶风格,  主动四轮转向,  直接横摆力矩,  相平面 
Fig.1 Overall structure diagram of AFWS/DYC stability coordination control considering driving styles
Fig.2 Driving style data collection platform
Fig.3 Lateral angular velocity test data of different drivers
标签X1X2X3X4X5
10.104 90.083 50.008 10.093 20.047 3
20.104 90.083 50.008 10.093 20.047 3
30.050 20.523 10.043 30.532 20.531 2
Tab.1 Driving style clustering centers
Fig.4 Driving style clustering results
Fig.5 Road setup of driver-in-the-loop tests
Fig.6 Driver-in-the-loop testing scenario
Fig.7 Vehicle stability factors for different driving styles
$ {v_{{x}}}{\text{/(km}}\cdot{{\text{h}}^{-1}}) $$\mu $${{{C}}_1}$${{{C}}_2}$
200.21.70.27
200.42.30.40
200.83.20.66
400.42.30.35
600.42.30.30
800.42.30.27
1000.42.30.27
Tab.2 Stability domain boundary coefficients for different vehicle speeds and road friction coefficients
Fig.8 Stability region partitioning in $\beta$-$\omega $ phase plane
Fig.9 Transition region partitioning in $\beta$-$\omega $ phase plane
Fig.10 Linear 2DOF vehicle model
Fig.11 Change curves of control output weight
参数取值参数取值
m/kg1 230${I_z}$/(kg·m2)1 343.1
b/m1.56$a$/m1.04
${k_{\mathrm{f}}}$/(N·rad?1)30 797${k_{\mathrm{r}}}$/(N·rad?1)30 797
Tab.3 Vehicle parameters
Fig.12 Steering wheel angle input
Fig.13 Driver open-loop test simulation results
模型$ {\omega _{{\text{max}}}} $ /(°·s?1)$ {\beta _{{\text{max}}}} $/(°)$ {\sigma _\omega } $ /(°·s?1)$ {\sigma _\beta } $/(°)
无控制13.042.069.091.15
二自由度模型10.511.128.740.70
谨慎型10.521.138.690.69
一般型10.521.148.760.71
激进型10.541.188.940.77
Tab.4 Maximum values and standard deviations of state variables for different reference models
Fig.14 Driver in-loop testing platform
Fig.15 In-loop testing results for different drivers under medium speed and low adhesion conditions
模型$ {\omega _{{\text{max}}}} $ /(°·s?1)$ {\beta _{{\text{max}}}} $ /(°)$ {\delta _{{\text{max}}}} $ /(°)
谨慎型无控制10.372.2556.43
谨慎型6.760.6066.24
一般型无控制11.152.3154.59
一般型6.210.6166.78
激进型无控制20.644.7372.85
激进型9.831.2265.03
Tab.5 Maximum values of state variables before and after applying stability control to vehicles driven by different drivers under medium speed and low adhesion conditions
Fig.16 In-loop testing results for different drivers under high-speed and high adhesion conditions
模型$ {\omega _{{\text{max}}}} $ /(°·s?1)$ {\beta _{{\text{max}}}} $ /(°)$ {\delta _{{\text{max}}}} $ /(°)
谨慎型无控制5.971.2462.96
谨慎型9.620.6938.93
一般型无控制8.791.1159.13
一般型8.281.1443.14
激进型无控制18.142.83129.38
激进型14.472.4881.90
Tab.6 Maximum values of state variables before and after applying stability control to vehicles driven by different drivers under high speed and high adhesion conditions
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