基于改进粒子群算法的车辆被动悬架优化与仿真研究

Optimization and simulation of vehicle passive suspension based on improved particle swarm optimization algorithm

针对车辆行驶过程中容易受到地面白噪声影响问题,创建了四自由度四分之一车辆被动悬架模型,推导出车辆垂直方向动力学方程式。构造了混合目标函数,采用改进粒子群算法对四自由度四分之一车辆被动悬架模型约束参数进行优化。建立车辆被动悬架系统仿真模型,在不同行驶速度下,通过Matlab/Simulink软件进行动力学仿真,与优化前仿真结果进行对比。仿真结果显示,优化后车辆被动悬架驾驶员头部垂直方向加速度、簧载垂直方向行程及非簧载垂直方向位移的均方根最大值分别减少了51.0%、45.4%和40.7%,车辆垂直方向振动峰值明显降低。采用改进粒子群算法优化车辆被动悬架系统,可以提高车辆行驶的稳定性,改善驾驶员乘坐的舒适度。

Aiming at the problem of white noise, the vehicle passive suspension model with four degrees of freedom is created, and the dynamic equation of the vehicle＇ s vertical direction is derived. The hybrid objective function is constructed, and the improved particle swarm optimization algorithm is used to optimize the parameters of the passive suspension model of four degrees of free- dom quarter vehicle. The vehicle passive suspension system simulation model was established, and the dynamic simulation was carried out by Matlab/Simulink software at different speed, and the simulation results are compared with those before optimiza- tion. The simulation results show that the root mean square after optimization of vehicle passive vertical suspension driver head ac- celeration, sprung vertical stroke and unsprung vertical displacement of the maximum value were reduced by 51% ,45.4 % and 40.7 %, and the peak vibration of vehicle vertical direction was also reduced. Improved particle swarm optimization algorithm is used to optimize the vehicle passive suspension system, which can improve the stability of vehicle driving and improve the ride comfort of the vehicle.