Model Prediction Control Path Tracking Algorithm Based on Adaptive Stanley

Path tracking is an important part of autonomous vehicles. Stanley algorithm is widely used in path tracking control of front wheel steering vehicles. The traditional Stanley algorithm calculates the front wheel angle according to the relative geometric relationship between the vehicle pose and the reference path point, which depends on the nearest reference path point. It is suitable for the low-speed and small change in curvature of reference paths. In order to improve the tracking accuracy and stability of Stanley algorithm, a model predictive control path tracking algorithm based on adaptive Stanley is proposed, which considers the adaptive change of preview distance and vehicle dynamics. The comparative simulation analysis of the proposed control strategy and the traditional Stanley control shows that the model predictive control path tracking algorithm based on adaptive Stanley can maintain high trajectory tracking accuracy and vehicle stability at high speed.