关键词: 自动驾驶车辆, 路径跟踪, 模型预测控制, 自适应, 权重系数, 模糊控制

Abstract:

In order to improve the path tracking accuracy and driving stability of autonomous vehicles under different operating conditions, a path tracking control algorithm based on vehicle dynamics model and model predictive control theory is proposed, which adaptively adjusts the weight coefficient of the cost function according to tracking deviation and road curvature. The weight coefficient matrix of the traditional MPC path tracking controller is dynamically optimized by fuzzy controller, so that when the deviation between the vehicle and the reference path is relatively large, the deviation can be quickly reduced to ensure the safety of the vehicle. When the path tracking deviation is relatively small and the reference path curvature is relatively small, the system puts more emphasis on the requirements of driving stability. In order to verify the performance of the designed path tracking controller, CarSim/Simulink co-simulation model is built. Compared with MPC path tracking controller with constant weight coefficient, the tracking accuracy and stability of MPC path tracking controller with adaptive weight coefficient are improved.

Key words: autonomous vehicles, path tracking, model predictive control, adaptive, weight coefficient, fuzzy controller