摘要
为解决线性二次型调节器(LQR)中的系数矩阵Q和R选取困难的问题,针对LQR算法进行优化。基于模拟退火算法和粒子群算法提出模拟退火粒子群优化(SA-PSO)算法,并且对于学习因子提出线性异步变化优化方法提高算法的搜索效率。基于二自由度动力学模型建立前馈LQR,加入预测模型模块提高模型控制精度。利用LQR的代价函数作为适应度函数优化权重参数矩阵,分析对比提出的SA-PSO算法和粒子群(PSO)优化算法的跟踪控制效果。通过Simulink/Carsim联合仿真实验,结果表明:SA-PSO算法相较于PSO算法在同等横向控制精度前提下前轮转角控制降低28.83%,相较于固定权值LQR,其前轮转角控制降低44.58%。提高了汽车行驶时的稳定性和平滑性,且具有较高鲁棒性。
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.
作者
黄益绍
郭钦
周润湘
HUANG Yishao;GUO Qin;ZHOU Runxiang(School of Traffic and Transportation Engineering,Changsha University of Science and Technology,Changsha 410114,China)
出处
《重庆理工大学学报(自然科学)》
北大核心
2025年第8期19-27,共9页
Journal of Chongqing University of Technology:Natural Science
基金
国家自然科学基金项目(51678075)
湖南省自然科学基金项目(2022JJ30619)。
作者简介
黄益绍,男,博士,副教授,主要从事交通信息工程与控制研究,E-mail:744861302@qq.com;通信作者:郭钦,男,硕士研究生,主要从事智能交通控制研究,E-mail:1939867227@qq.com。
