摘要
建立1/4车辆主动悬架模型,提出全参数自适应粒子群LQR主动悬架控制策略,以提高汽车操纵稳定性。针对传统LQR控制策略参数调整困难,对原始粒子群算法进行自适应改进,考虑多个悬架性能指标,建立目标函数优化得到最优权重矩阵参数,以提升控制性能。通过Matlab/Simulink平台不同路面模型输入下联合仿真,与被动悬架、传统LQR控制等对比,结果表明,所提出的全参数自适应粒子群算法相较于原始算法收敛效率更高。经优化的控制策略显著改善了汽车主动悬架性能指标,与传统LQR主动悬架控制器相比,在稳定时间及极值等方面平均优化幅度40%以上,大幅提升乘坐舒适性。
A quarter-car active suspension model is built,and a fully parameterized adaptive particle swarm optimization(APSO)LQR control strategy is proposed to enhance vehicle handling stability.To remedy the difficulties in adjusting parameters in traditional LQR control strategies,the original particle swarm algorithm is adaptively improved with the consideration of multiple suspension performance indicators.By optimizing the objective function,the optimal weight matrix parameters are obtained to enhance control performance.Joint simulations are conducted on the Matlab/Simulink platform with various road input models,and the proposed APSO algorithm is compared with passive suspension and traditional LQR control.The results show APSO algorithm achieves higher convergence efficiency than the original algorithm.Compared with the traditional LQR active suspension controllers,the optimized control strategy markedly improves the performance indicators of the active suspension system.It achieves a marked improvement of over 40%in stability time and extrema,significantly enhancing ride comfort.
作者
詹长书
陈小文
ZHAN Changshu;CHEN Xiaowen(School of Mechanical and Electrical Engineering,Northeastern Forestry University,Harbin 150040,China)
出处
《重庆理工大学学报（自然科学）》
CAS
北大核心
2023年第12期9-17,共9页
Journal of Chongqing University of Technology：Natural Science
基金
中央高校基本科研业务费专项(2572018BG02)。
