多铰接车辆路径跟踪控制及仿真分析

doi:10.16182/j.issn1004731x.joss.23-0621

摘要/Abstract

摘要：

针对多铰接车辆的车身结构限制了车辆的灵活性，导致后车在行驶过程中容易出现路径偏移的问题，提出一种以理想铰接角为控制目标的前馈加反馈路径跟踪控制 方法，通过最小化理想与实际铰接角的偏差来实现后车对期望路径的准确跟踪。根据车辆和期望路径的几何位置关系，从适用范围和误差累积两个角度改进了传统的理想铰接角计算方法；基于车辆理想铰接角，考虑了控制方法的适用范围，设计了不依赖具体模型的前馈加径向基（RBF）神经网络PID反馈控制器；搭建了TruckSim和MATLAB/Simulink多铰接车辆联合仿真平台，比较分析了不同工况下车辆的路径跟踪性能。仿真结果表明：所提方法对不同工况具有良好的适用性和较高的跟踪精度，有效地降低了后车的误差累积。

关键词: 多铰接车辆, 路径跟踪, 理想铰接角, RBF神经网络, 联合仿真

Abstract:

The structure of multi-articulated vehicle body limits the flexibility of the vehicle and causes the deviation of the rear vehicle. Taking the ideal articulation angle as the control target, a feedforward plus feedback path following control method is proposed, which realizes the precise path following of rear vehicle bodies by minimizing the deviation between the ideal articulation angle and the actual articulation angle. According to the geometric position relationship between the vehicle and the desired path, the traditional calculation method of the ideal articulation angle is improved from two perspectives of application range and error accumulation.Based on the ideal articulation angle of the vehicle and the applicable scope of the control method, a feedforward plus radial basis function (RBF) neural network PID feedback controller is designed, which do not depend on the specific model. The co-simulation platform TruckSim and MATLAB/Simulink is built to compare and analyze the path following performance of vehicles under different working conditions. The simulation results show that the proposed control method has good applicability and higher following accuracy in different working conditions, and effectively reduces the error accumulation of the rear vehicle.

Key words: multi-articulated vehicles, path following, ideal articulation angle, RBF neural network, co-simulation

中图分类号:

U469.5

赵煜,杨蔡进,王谭明等 . 多铰接车辆路径跟踪控制及仿真分析[J]. 系统仿真学报, 2024, 36(10): 2300-2313.

Zhao Yu,Yang Caijin,Wang Tanming,et al . Path Following Control and Simulation Analysis of Multi-articulated Vehicles[J]. Journal of System Simulation, 2024, 36(10): 2300-2313.

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车辆参数	量值
首车质量m₁/kg	8 935
中间车质量m₂/kg	9 275
尾车质量m₃/kg	9 275
首车质心距第一轴距离a₁/m	1.8
首车质心距第二轴距离b₁/m	3.4
第一铰接点距第一轴距离l₁/m	7.45
第一铰接点距第三轴距离l₂/m	7.45
中间车质心距第三轴距离b₂/m	3.5
第二铰接点距第三轴距离d₂/m	2.15
第二铰接点距第四轴距离l₃/m	7.45
尾车质心距第四轴距离b₃/m	3.5

参数		首车轨迹	参考路径	对比结果/%
第三轴横向偏差/cm	max	6.392	5.042	-21.12
第三轴横向偏差/cm	RMS	3.821	0.967	-74.69
第四轴横向偏差/cm	max	12.312	6.182	-49.79
第四轴横向偏差/cm	RMS	4.962	1.376	-72.27

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