考虑复杂扰动的轻型商用车路径跟踪混合控制方法

A Hybrid Control Strategy for Light Commercial Vehicle Path Tracking Considering Complex Disturbances

外界扰动、模型不确定性和参数摄动等复杂扰动直接影响智能车辆路径跟踪控制的精度和行驶安全性。商用车的载重特性使其在行驶过程中受到的复杂扰动更为明显,为提升自动驾驶商用车路径跟踪精度和平顺性,提出一种路径跟踪混合控制方法。首先,建立鲁棒性强的基于扩张观测器的滑模控制器和变化平稳的增量式LQR控制器,其中增量式LQR的参数使用粒子群算法整定。然后,使用模糊控制器将两者相结合,根据车速和横向误差调整权重系数,在提升系统鲁棒性的同时削弱抖振。最后,进行了仿真分析和实车试验。试验数据表明,SMC+LQR具备较好的控制效果,能应对外界的复杂扰动。

Complex disturbances such as external interference,model uncertainty and parameter perturbation directly affect the accuracy and driving safety of intelligent vehicle path tracking control.Commercial vehicles are more susceptible to complex disturbances during driving because of their load characteristics.A hybrid path tracking control method is proposed in order to improve the accuracy and smoothness of commercial vehicle path tracking.Firstly,a robust sliding mode controller based on extended observer and an incremental LQR controller with stable changes are established.Particle swarm optimization algorithm is used to tune the parameters of the incremental LQR.Then,in order to improve robustness while weakening chattering,a fuzzy controller is used to adjust weight coefficient between them according to vehicle speed and lateral error.Finally,simulation analysis and vehicle experiments are conducted.The experimental data shows that SMC+LQR has good control performance to cope with complex external disturbances.