轨道车辆蛇行运动GA-LQR主动控制研究

Active control analysis of railway vehicle hunting motion based on LQR and genetic algorithm

基于简化低自由度车辆模型与整车蛇行运动模型,研究性能指标对车辆主动悬挂LQR控制效果差异,利用遗传算法迭代得到最优的LQR控制器。研究结果表明:利用简化1/4车辆模型与整车模型设计出的主动控制器控制效果相差很大;性能指标的选择对车辆悬挂优化控制至关重要;对比2种不同性能指标条件,车体前、后端Sperling指数均有明显下降,而合理的性能指标会达到更加优异的主动控制效果,可以在较大运行速度范围内提高系统的平稳性。

Optimal LQR control effects of active suspension on performance index were compared by simplified and the whole vehicle model considering track irregularity respectively.The optimal LQR controller was iteratively calculated by genetic algorithm.The results show that the active control effect designed by simplified vehicle model differs greatly from that of the whole vehicle model.Furthermore,performance index is very important for vehicle suspension control.Under two performance indexes,Sperling indexes at the front and rear end of the car body decrease significantly,Besides,the car body stability is greatly improved if performance index is properly selected during a large speed scale.