Path tracking control of unmanned truck based on genetic algorithm
Path tracking is essential for unmanned driving.This article presents the design of a path tracking system for unmanned trucks,aiming to enhance accuracy and stability across various speeds.The system employs a Linear Quadratic Regulator(LQR)optimized through an improved Genetic Algorithm(GA).First,a two-degree-of-freedom dynamic model and a tracking error model of the vehicle are established based on natural coordinate system.Subse-quently,an LQR controller is designed to eliminate steady-state errors and enhance tracking accuracy through feed-forward control.Second,the genetic algorithm is enhanced to optimize the weight matrix of the LQR controller,resul-ting in improved accuracy and stability for path tracking.Finally,the control effectiveness of the designed LQR con-troller is simulated and verified across a range of operating conditions using the joint simulation platform of Matlab/Simulink and TruckSim.The results show that the GA-optimized LQR(Linear Quadratic Regulator)controller im-proves the tracking accuracy by about 68.5％and 49.4％at speeds of 30 km/h and 60 km/h,respectively,under the double lane change scenario;while under the U-turn scenario,the tracking accuracy is enhanced by approxi-mately 12.0％and 25.5％,respectively.Specifically,it demonstrates higher stability,with position and heading errors controllable within 0.17 m and 0.11 rad,respectively,thereby validating the efficacy of the proposed tracking control scheme.
万方数据
维普
