基于粒子群寻优的汽车主动悬架滚动时域控制

Receding Horizon Control of Vehicle Active Suspension Based on Particle Swarm Optimization

为了提升汽车主动悬架系统控制精度,设计了一种基于粒子群寻优的汽车主动悬架滚动时域控制算法。建立主动悬架单轮车辆动力学模型,定义综合描述车身垂向振动加速度、悬架动行程和轮胎动位移的主动悬架性能优化目标函数和输入/输出约束条件,在此基础上研究主动悬架系统滚动时域优化控制算法,建立主动悬架做动器控制力在线优化问题,通过粒子群优化算法求解最优控制律。MATLAB/Simulink仿真结果表明,基于粒子群算法求解的滚动时域控制律能有效提高主动悬架系统的综合性能。

In order to improve the control accuracy of active suspension system,a receding horizon control algorithm based on particle swarm optimization for active suspension system is designed.The dynamic model of active suspension with single-wheeled vehicle is established.The objective function and input/output constraints for the performance optimization of active suspension are defined,which comprehensively describes the vertical vibration acceleration of vehicle body,the dynamic travel of suspension and the dynamic displacement of tire.On this basis,the receding horizon control algorithm of active suspension system is investigated.On-line optimization of active suspension actuator control force is established and optimal control law is solved by particle swarm optimization algorithm.The simulation results of MATLAB/Simulink show that the receding horizon control law based on particle swarm algorithm can effectively improve the comprehensive performance of active suspension system.