An Adaptive MPC Trajectory Tracking Algorithm For Autonomous Vehicles

An adaptive trajectory tracking control algorithm based on the vehicle dynamics model and model predictive control (MPC) is designed to improve the accuracy of trajectory tracking and the ride comfort and vehicle stability of the driver. The algorithm is mainly due to the different changes of transverse speed and longitudinal speed under different road conditions. The weight coefficient matrix in model predictive control is optimized in real-time and online through fuzzy control so that the controller can quickly reduce the tracking error and ensure the accuracy of track tracking when the tracking track changes greatly. When the tracking track changes little, it can reduce the change of the front wheel angle of the vehicle, ensure the stability of the vehicle and improve the comfort of passengers. The results show that the controller can update the weight coefficient in real-time with the change of vehicle speed, and improve the accuracy and stability of vehicle trajectory tracking.