Cascade Optimization Control of Unmanned Vehicle Path Tracking Under Harsh Driving Conditions  

作　　者：黄迎港 罗文广 黄丹 蓝红莉 HUANG Yinggang;LUO Wenguang;HUANG Dan;LAN Hongli(School of Electrical and Information Engineering、Guangxi Key Laboratory of Auto Parts and Vehicle Technology,Guangxi University of Science and Technology,Liuzhou 545006,Guangxi,China;School of Mechanical and Automotive Engineering,South China University of Technology,Guangzhou 510641,China)

机构地区：[1]School of Electrical and Information Engineering、Guangxi Key Laboratory of Auto Parts and Vehicle Technology,Guangxi University of Science and Technology,Liuzhou 545006,Guangxi,China [2]School of Mechanical and Automotive Engineering,South China University of Technology,Guangzhou 510641,China

出　　处：《Journal of Shanghai Jiaotong university(Science)》2023年第1期114-125,共12页上海交通大学学报（英文版）

基　　金：the Natural Science Foundation of Guangxi(No.2020GXNSFDA238011);the Open Fund Project of Guangxi Key Laboratory of Automation Detection Technology and Instrument(No.YQ21203);the Independent Research Project of Guangxi Key Laboratory of Auto Parts and Vehicle Technology(No.2020GKLACVTZZ02)。

摘　　要：Under ultra-high-speed and harsh conditions,conventional control methods struggle to ensure the path tracking accuracy and driving stability of unmanned vehicles during the turning process.Therefore,this study proposes a cascade control to solve this problem.Based on the new vehicle error model that considers vehicle tire sideslip and road curvature,the feedforward-parametric adaptive linear quadratic regulator(LQR)and proportional integral control-based speed-keeping controllers are used to compose the path-tracking cascade optimization controller for unmanned vehicles.To improve the adaptability of the unmanned vehicle path-tracking control under harsh driving conditions,the LQR controller parameters are automatically adjusted using a back-propagation neural network,in which the initial weights and thresholds are optimized using the improved grey wolf optimization algorithm according to the driving conditions.The speed-keeping controller reduces the impact on the curve-tracking accuracy under nonlinear vehicle speed variations.Finally,a joint model of MATLAB/Simulink and CarSim was established,and simulations show that the proposed control method can achieve stable entry and exit curves at ultra-high speeds for unmanned vehicles.Under strong wind and ice road conditions,the method exhibits a higher tracking accuracy and is more adaptive and robust to external interference in driving and variable curvature roads than methods such as the feedforward-LQR,preview and pure pursuit controls.

关 键 词：unmanned vehicles path tracking harsh driving conditions cascade control improved gray wolf optimization algorithm backpropagation neural network 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置]