基于加速粒子群算法的车辆座椅悬架最优控制研究

针对传统最优线性二次型控制器中加权矩阵往往由设计者根据经验确定的问题,提出一种应用加速粒子群算法确定加权矩阵的方法。建立"车轮-车身-座椅、人体"6自由度随机振动系统模型,采用加速粒子群算法对座椅悬架进行参数优化,并对优化后系统进行最优线性二次型控制。将基于加速粒子群算法的最优线性二次型座椅悬架系统中"座椅、人体"垂向加速度与初始系统及基于常规粒子群算法和遗传算法的最优线性二次型控制系统进行对比,验证了此控制系统的有效性和优越性。

Optimal Control of Active Seat Suspension Systems using Acceleration based Particle Swarm Optimization

Weighting matrices of most standard LQR controllers are determined by the designers according to their experience, and that usually makes controllers unable to achieve global optimum. To deal with this problem, a method of determining weighting matrices by acceleration based particle swarm optimization (APSO) was proposed. A six degree of freedom (DOF) half car model with “seat-human” system was established in this paper. The parameters of the seat suspension were optimized by APSO, and LQR control was carried out on the basis of the parameter optimization system. In this study, MATLAB/Simulink is used for the simulation of parameter optimization system and LQR control system. The vibration isolation performance of seat suspension system is indicated by the vertical acceleration of “seat-human”. Results show that the controller based on APSO has better vibration-reducing properties than the LQR controller based on GA.