低附着下分布式驱动车辆的路径跟踪与横向稳定性控制方法

杨炜; 谭亮; 杜亚峰; 孙雪; 张宇杰

doi:10.3963/j.jssn.1674-4861.2023.06.007

低附着下分布式驱动车辆的路径跟踪与横向稳定性控制方法

doi: 10.3963/j.jssn.1674-4861.2023.06.007

杨炜^1, ,,
谭亮¹,
杜亚峰¹,
孙雪¹,
张宇杰²

1.
长安大学汽车学院西安 710061
2.
智己汽车科技有限公司上海 201800

基金项目:

国家重点研发计划项目 2021YFE0203600

陕西省自然科学基金青年项目 2017JQ6045

详细信息

通讯作者:
杨炜（1985—），博士，讲师. 研究方向：汽车主动安全技术、智能网联汽车，E-mail: yw@chd.edu.cn

中图分类号: U471.15
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- 被引次数: 0
出版历程
- 收稿日期: 2023-05-09
- 网络出版日期: 2024-04-03

Path Tracking and Lateral Stability Control for Distributed Drive Vehicles with Low Adhesion

1.
School of Automobile, Chang'an University, Xi'an 710061, China
2.
Zhiji Automotive Technology Co. Ltd., Shanghai 201800, China

摘要

摘要: 由于车辆在低附着工况（如积雪、潮湿）下跟踪性与横向稳定性的耦合关系，这使得二者之间的控制难以同时满足跟踪精度及良好的稳定性需求，因此，研究了基于分布式独立驱动电动汽车平台的路径跟踪与横向稳定性联合控制模型。对于路径跟踪问题，采用了横纵向解耦控制；对于横向跟踪控制问题，模型采用基于Frenet坐标系的模型预测控制（model predictive control，MPC）路径跟踪控制方法，并引入了转角补偿策略以提升路径跟踪的准确性；对于纵向车速控制问题，模型利用MPC求解期望加速度，并根据行驶平衡方程和保证路面附着最大利用率的条件下确定电机扭矩输出，实现对纵向车速的控制。对于横向稳定性控制问题，提出了基于稳定性增强系统（stability augmentation system，STA）的横摆力矩控制模型，在获得附加力矩后，以二次规划方法将其合理分配到各个车轮上，从而增强了车辆的横向稳定性。最后，通过CarSim/Simulink联合仿真平台，在双移线道路工况下进行了仿真验证。结果表明：在积雪路面，改进模型相比传统MPC在保证横向误差接近的条件下，最大的质心侧偏角降低了83.1%；在潮湿路面，改进模型相比传统MPC模型最大横向误差降低了52.2%，最大质心侧偏角降低了83.3%；相比于传统滑膜，本文模型在跟踪误差与质心侧偏角占优势的情况下，有效的抑制了震荡现象。通过联合控制，可以加强车辆在低附着路面的稳定性与安全性。
- 智能交通 /
- 无人驾驶 /
- 四驱车 /
- 横向稳定性 /
- 模型预测控制 /
- 滑模控制
Abstract: Due to the coupling relationship between tracking and lateral stability of vehicles under low adhesion conditions (such as snow and moisture), it is difficult to control both tracking accuracy and good stability simultaneously. Therefore, a joint control model of path tracking and lateral stability is proposed based on distributed independent drive electric vehicle platform. The transverse and longitudinal decoupling control is adopted for the path tracking problem. Besides, the model predictive control (MPC) method based on Frenet coordinate system is adopted for the horizontal tracking control problem, and angle compensation strategy is introduced to improve the accuracy of path tracking. For the longitudinal speed control problem, the model uses MPC to solve the expected acceleration, and determines the motor torque output according to the driving balance equation and the maximum utilization rate of road adhesion, so as to achieve the longitudinal speed control. For lateral stability control, a yaw torque control model based on stability augmentation system (STA) is proposed. After additional torque is obtained, it is effectively distributed to each wheel by quadratic programming method, thus enhancing the lateral stability of the vehicle. Moreover, the CarSim/Simulink co-simulation platform is used to simulate and verify the double-shift road conditions. The results show that under the condition of snow-covered pavement, the maximum lateral deflection angle of the improved model is reduced by 83.1% compared with the traditional MPC under the condition that the lateral error is close. Under wet road conditions, the maximum lateral error and the maximum lateral deflection angle of the improved model are reduced by 52.2% and 83.3%, respectively, compared with the traditional MPC model. Compared with the traditional synovial model, this model can effectively suppress the oscillation phenomenon when the tracking error and the side deflection angle of the center of mass are dominant. Through the joint control, the stability and safety of the vehicle on the low adhesion road surface can be enhanced.
- intelligent transportation /
- driverless /
- four-wheel drive /
- lateral stability /
- model predictive control /
- sliding mode control

HTML全文

图 1 车辆动力学模型

Figure 1. Vehicle dynamics model

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图 2 Frenet坐标系下误差关系

Figure 2. Error relationship in Frenet coordinate system

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图 3 基于闭环PID转角补偿的MPC控制策略

Figure 3. MPC control strategy based on closed-loop PID angle compensation

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图 4 超螺旋算法相轨迹

Figure 4. Supercoiled algorithm phase trajectories

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图 5 μ = 0.3，v_x = 65 km/h匀速工况

Figure 5. μ = 0.3, v_x = 65 km/h constant speed working condition

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图 6 误差示意图

Figure 6. Error schematic

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图 7 μ = 0.6，初速度54 km/h加速工况

Figure 7. μ = 0.6, initial speed 54 km/h acceleration condition

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