多铰接车辆路径跟踪控制及仿真分析
Path Following Control and Simulation Analysis of Multi-articulated Vehicles
赵煜 1杨蔡进 1王谭明 1徐菁 1周帅1
作者信息
- 1. 西南交通大学 轨道交通运载系统全国重点实验室,四川 成都 610031
- 折叠
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.
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.
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Key words
multi-articulated vehicles/path following/ideal articulation angle/RBF neural network/co-simulation引用本文复制引用
基金项目
国家重点研发计划(2018YFB101603-06)
四川省科技计划(2020JDRC0008)
四川省科技计划(2020YFG0023)
出版年
2024
NETL
NSTL
万方数据