重庆理工大学学报2025，Vol.39Issue(15)：19-27,9.DOI:10.3969/j.issn.1674-8425(z).2025.08.003
基于SA-PSO轨迹追踪LQR横向控制优化
LQR lateral control optimization based on SA-PSO trajectory tracking
摘要
Abstract
To address the difficulty in choosing coefficient matrix Q and R in linear quadratic adjustment(LQR),this paper optimizes LQR algorithm.A simulated annealing particle swarm optimization(SA-PSO)algorithm is proposed based on simulated annealing algorithm and particle swarm optimization algorithm,and a linear asynchronous change optimization method for learning factors is proposed to improve the search efficiency of the algorithm.Based on the two-degree-of-freedom dynamic model,the feedforward LQR is built,and the predictive model module is added to improve the control accuracy of the model.The cost function of LQR is employed as fitness function to optimize the weight parameter matrix,and the tracking control effect of the proposed SA-PSO algorithm and particle swarm optimization(PSO)algorithm is analyzed and compared.Simulink/Carsim co-simulation results show the SA-PSO algorithm reduces the front wheel angle control by 28.83％compared with the PSO algorithm under the same lateral control accuracy.Compared with the fixed weight LQR,the front wheel angle control is reduced by 44.58％.The algorithm improves the stability and smoothness of the car and achieves high robustness.关键词
汽车工程/横向控制/粒子群算法优化/线性二次型调节器(LQR)/轨迹跟踪Key words
ITS/lateral control/particle swarm optimization/LQR/trajectory track分类
交通工程引用本文复制引用
黄益绍,郭钦,周润湘..基于SA-PSO轨迹追踪LQR横向控制优化[J].重庆理工大学学报,2025,39(15):19-27,9.基金项目
国家自然科学基金项目(51678075) （51678075）
湖南省自然科学基金项目(2022JJ30619) （2022JJ30619）
