分布式电驱车路径跟踪分层协调控制方法研究

周枫林; 钦宇; 游雨龙; 邹腾安; 李光; 张智永; 常雨康

doi:10.13433/j.cnki.1003-8728.20220012

分布式电驱车路径跟踪分层协调控制方法研究

doi: 10.13433/j.cnki.1003-8728.20220012

1.
湖南工业大学机械工程学院, 湖南株洲 412007
2.
国防科技大学智能科学学院, 长沙 410001
3.
湖南宇正智能科技有限公司, 长沙 410073

基金项目:

湖南省自然科学基金项目 2021JJ30211

湖南省自然科学基金项目 2021JJ50043

详细信息

作者简介:
周枫林(1986-), 副教授, 博士后, 研究方向为装备智能化设计制造与控制技术, 164499487@qq.com

中图分类号: TP273
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- 被引次数: 0
出版历程
- 收稿日期: 2021-04-02
- 刊出日期: 2023-07-25

A Hierarchical Coordinated Control Method for Tracking Path of Distributed Electric Vehicle

1.
School of Mechanical Engineering, Hunan University of Technology, Zhuzhou 412007, Hu′nan, China
2.
School of Intelligent Science, National University of Defense Technology, Changsha 410001, China
3.
Hunan Yuzheng Intelligent Technology Co., Ltd., Changsha 410073, China

摘要

摘要: 针对分布式电驱车路径跟踪问题，基于分层协调控制方法，提出了一种路径跟踪策略。由电驱车独立转向/驱动的结构优势，设计四轮阿克曼转向理论，以建立电驱车路径跟踪分层运动学模型，并应用到路径跟踪控制策略中。该策略分为上下两层控制。在上层控制中，将上层运动学模型作为模型预测控制算法的预测模型，通过设定最优目标函数和约束条件将未来控制增量的求解问题转换为二次规划的最优解问题，计算出最优转角和速度控制量。下层控制中，通过下层运动学，将上层控制得到的控制量映射到四轮的转角和速度控制量，应用模糊PID算法，实现电驱车的路径跟踪控制。在基于Carsim/Simulink的仿真平台上进行圆形路径跟踪仿真验证，结果表明，该控制器能够使分布式电驱车实现路径的准确跟踪；在实车试验中进行换道路径跟踪，简单MPC(模型预测控制算法)与分层协调控制数据结果对比表明分层协调控制方法能够有效的改善控制性能，提供路径跟踪的精确性和稳定性。
- 分布式电驱车 /
- 分层协调控制方法 /
- 分层运动学 /
- 路径跟踪
Abstract: Because the path of a distributed electric vehicle is difficult to track, a path tracking strategy was proposed based on the layered coordinated control method. Based on the structural advantage of the electric vehicle′s independent steering/driving, the four-wheel Ackerman steering theory is designed to establish the layered kinematics model of the electric vehicle′s path following and to apply it to the path following control strategy. The strategy is divided into two layers of control. In the upper layer control, the upper-layer kinematics model is used as the prediction model of the model predictive control algorithm. By setting the optimal objective function and constraint conditions, the solution of the future control increment problem is transformed into the optimal quadratic programming solution problem. The optimal rotation angle and velocity control quantity are calculated. In the lower layer control, through the lower layer kinematics, the control quantity obtained with the upper layer control is mapped to the angle and speed control quantity of the four wheels of the electric vehicle. The fuzzy PID algorithm is used to realize the path tracking control of the electric vehicle. The circular path tracking is simulated with the CarSim/Simulink simulation platform. The simulation results show that the controller can accurately track the path of the distributed electric vehicles. The comparison between the simple MPC data and the layered coordinated control data shows that the layered coordinated control method can effectively improve the control performance, accuracy and stability of the path tracking.
- distributed electric vehicle /
- hierarchical coordinated control method /
- hierarchical kinematics /
- path tracking

HTML全文

图 1 分层协调控制理论下的分布式人车路径跟踪控制框架

Figure 1. Distributed manned vehicle path tracking control framework under layered coordinated control theory

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图 2 全电驱分布式电驱车动力学模型

Figure 2. Dynamic model of a fully electric-driven distributed electric vehicle

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图 3 MATLAB/Simulink下的仿真模块

Figure 3. MATLAB/Simulink Simulation module

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图 4 电驱车6种转向运动学分析

Figure 4. Analysis of 6 steering motions of electric vehicles

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图 5 分布式电驱车路径跟踪运动学模型

Figure 5. Kinematic model for path tracking of distributed electric vehicles

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图 6 四轮阿克曼转向模型

Figure 6. Four-wheel Ackermann steering model

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图 7 自适应模糊PID控制器流程示意图

Figure 7. Flowchart of the adaptive fuzzy PID controller

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图 8 基于自适应模糊PID控制的附加横摆力矩控制器

Figure 8. Additional yaw moment controller based on adaptive fuzzy PID control

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图 9 参数自整定模糊PID工作流程图

Figure 9. Workflow diagram of self-tuning fuzzy PID

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图 10 自适应模糊PID控制中ΔK_p输出曲面

Figure 10. The ΔK_p output surface of adaptive fuzzy PID control

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图 11 自适应模糊PID控制中ΔK_i输出曲面

Figure 11. The ΔK_i output surface of adaptive fuzzy PID control

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图 12 自适应模糊PID控制中ΔK_d输出曲面

Figure 12. The ΔK_d output surface of adaptive fuzzy PID control

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图 13 不同圆形半径路径跟踪效果对比图

Figure 13. Comparison of path tracking effects for different circular radii

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图 14 不同速度下的路径跟踪效果图

Figure 14. Path tracking effects at different speeds

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图 15 半径为15 m, 速度36 km/h的跟踪状态信息

Figure 15. Tracking state information for a radius of 15 m and a speed of 36 km/h

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图 16 路径跟踪状态信息误差图

Figure 16. Error graph of path tracking state information

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图 17 整车控制逻辑

Figure 17. Overall vehicle control logic

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图 18 期望斜形换道路径

Figure 18. Desired slalom lane change path

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图 19 测试路面期望路径标记

Figure 19. Marking of the desired path on the test road surface

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图 20 MPC控制下四轮滑移率

Figure 20. Four-wheel slip ratio under MPC control

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图 21 分层协调控制下四轮滑移率

Figure 21. Four-wheel slip ratio under layered coordinated control

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图 22 分层协调控制和MPC控制下速度跟踪对比

Figure 22. Comparison of speed tracking under layered coordinated control and MPC control

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图 23 分层协调控制和MPC控制下路径跟踪对比

Figure 23. Comparison of path tracking under layered coordinated control and MPC control

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