基于粒子群优化的主动悬架最优控制研究  

Research on Optimal Control of Active Suspension Based on Particle Swarm Optimization

作　　者：张玉分 卢家暄 龙金莲 李婧[1] 

机构地区：[1]贵州大学电气工程学院,贵州贵阳550025 [2]贵州大学大数据与信息工程学院,贵州贵阳550025

出　　处：《贵州大学学报（自然科学版）》2017年第5期71-75,82,共6页Journal of Guizhou University:Natural Sciences

基　　金：国家自然科学基金项目资助(51567005)

摘　　要：针对主动悬架最优控制器LQG的加权矩阵Q和R参数主要由人工调整来确定,不仅费时,而且无法保证获得最优的权重矩阵。本文采用粒子群算法对LQG的控制参数进行优化。通过利用粒子群算法的全局搜索能力,以主动悬架性能指标为目标函数对加权矩阵进行优化,以提高LQG的设计效率和性能。在Matlab/Simulink环境中进行仿真分析,结果表明:与传统的LQG控制比较,基于粒子群优化的LQR控制器使主动悬架的车身垂直加速度、悬架动行程和轮胎动位移的均方根值均得到降低,可以使车辆获得更优的乘坐舒适性和操作稳定性。The weighting matrix Q and R parameters for the active controller LQG （ linear-quadratic-Gaussian control） are mainly determined by manual adjustment, which is time-consuming and unable to guarantee the opti-mal weight matrix. In this paper, the control parameters of LQG were optimized by particle swarm optimization al-gorithm. With the global search ability of particle swarm algorithm, the weighting matrix was optimized by using the active suspension performance as the objective function to improve the design efficiency and performance of LQG.Experimental results realized by Matlab/simulink simulations show that the particle swarm optimization for LQR controller has satisfied the root mean square value of the vertical acceleration of the vehicle body, the mov-ing stroke of the suspension and the tire moving displacement are reduced by comparing with the traditional LQG control, the result show that the vehicle can achieve better ride comfort and operational stability.

关 键 词：粒子群算法 线性二次型(LQG)控制器 主动悬架 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] U463.331[自动化与计算机技术—控制科学与工程]