分布驱动无人车在极限工况下的稳定跟踪控制

Stable Tracking Control of Distributed Driven Unmanned Vehicle in Limit Condition

针对分布式驱动无人车在低附着、高速的极限工况下行驶时偏离目标路径和失稳的现象,提出一种稳定-跟踪联合控制策略,策略包含路径跟踪控制和横向稳定控制,使无人车在提高跟踪精度的同时又使其稳定性得到保障。路径跟踪模块在汽车二自由度模型的基础上,建立了最优LQR路径跟踪控制器,并引入前馈用以改善稳态误差,其次考虑误差变化的规律和路-车位置关系以及驾驶员预瞄模型,设计了改进的权重参数自适应LQR控制器;稳定性模块基于可以减小系统抖振的滑模模糊控制原理,并结合汽车稳定性参数的实际值与期望值的差值,求解出横摆力矩。其次在分配各轮转矩时建立约束条件,使得轮胎负荷率最小用以提升汽车的操纵稳定性。CarSim/Simulink仿真表明在高速、低附着的换道工况下,改进后的LQR控制器有效地改善跟踪误差,具有良好的鲁棒性。且在加入稳定性控制器后,横摆角速度与质心侧偏角的调节时间变短,极大改善了无人车在极限工况下的稳定性。

In view of the phenomenon of the distributed driverless vehicle deviating from the target path and instability in the low attachment and high speed limit conditions,a stable tracking joint control strategy is proposed,which includes path tracking control and lateral stability control,which can ensure the stability of the unmanned vehicle while improving the tracking accuracy.Based on the two degree of freedom model of the vehicle,the path tracking module establishes the optimal LQR path tracking controller,and introduces feedforward to improve the steady-state error.Secondly,considering the law of error change,the relationship between road and vehicle position and the driver preview model,the improved adaptive LQR controller with weight parameters is designed;The stability module is based on the sliding mode fuzzy control principle which can reduce the chattering of the system,and combines the difference between the actual value and the expected value of the vehicle stability parameters to solve the yaw moment.Secondly,the constraint conditions are established when the torque of each wheel is distributed,which makes the tire load rate minimum to improve the vehicle handling stability.The simulation of Carsim/Simulink shows that the improved LQR controller can improve the tracking error effectively and has good robustness under the high speed and low attachment condition.After adding stability controller,the adjustment time of yaw rate and center of mass yaw angle becomes shorter,which greatly improves the stability of unmanned vehicle under the limit condition.