摘要
针对矿用卡车(简称“矿卡”)在复杂路径且泥泞坑洼路面条件下横向跟踪精度不高的问题,为提升控制算法对矿区复杂行驶环境的适应性,文章提出一种考虑多因素融合的自适应预瞄循迹控制算法。首先,根据车辆运动学建立基于预瞄的后轮反馈跟踪模型;然后,由轨迹预测方法预测车辆的运行轨迹,考虑参考轨迹与预测轨迹之间的累积跟踪误差、车辆转向响应延时等因素,构建自适应预瞄的多目标优化函数;最后,通过遗传算法(genetic algorithms,GA)对优化函数进行求解,输出最优预瞄点至后轮反馈控制器,实现车辆在全局路径跟踪过程的最优化控制。仿真和矿卡实车测试结果显示,当车辆起步初始横向误差小于0.8 m、横摆角误差小于5°时,采用自适应预瞄循迹控制算法能够实现较好的纠偏控制,最终停车时横向误差小于0.2 m,横摆角误差小于2°,有效提升了矿卡的循迹跟踪能力以及对复杂工况环境的适应性。
In the view that lateral tracking accuracy of mine trucks is not high under the condition of complex path and muddy road, in order to improve the adaptability of the control algorithm to the complex driving environment in the mining area, an adaptive preview tracking control algorithm considering multi-factor fusion is proposed in this paper. Firstly, according to the vehicle kinematics, a rear wheel feedback tracking model based on preview is established. Secondly, vehicle trajectory is predicted by the trajectory prediction method. Considering the cumulative tracking error between reference trajectory and predicted trajectory, vehicle steering response delay and other factors, a multi-objective optimization function of adaptive preview is constructed. Finally, the optimization function is solved by genetic algorithms (GA), and the optimal preview point is output to the rear wheel feedback controller to realize the optimal control of the vehicle in the global path tracking process. Simulation and real vehicle test results show that when a vehicle starts with an initial lateral error of less than 0.8 m and a yaw angle error of 5°, the adaptive preview tracking control algorithm can achieve a certain degree of deviation-correcting control;the vehicle stops with a lateral error of less than 0.2 m and a yaw angle error of less than 2°, which effectively improves the tracking ability of mine trucks and its adaptability to complex working conditions.
作者
黄耀然
刘智聪
康远荣
HUANG Yaoran;LIU Zhicong;KANG Yuanrong(CRRC Zhuzhou Institute Co.,Ltd.,Zhuzhou,Hunan 412001,China)
出处
《控制与信息技术》
2022年第5期53-59,共7页
CONTROL AND INFORMATION TECHNOLOGY
基金
国家重点研发计划(2021YFB2501802)。
关键词
自适应预瞄
无人驾驶
路径跟踪
轨迹预测
遗传算法
矿用卡车
adaptive preview
unmanned driving
path tracking
trajectory prediction
genetic algorithm
mine truck
