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.

I. Introduction

Compared with traditional vehicles, self-driving vehicles becomes a research hotspot due to it can reduce the occurrence of traffic accidents, alleviate traffic congestion, and increase traffic efficiency [1]. The path tracking system is an important technology of an autonomous vehicle. It calculates the steering wheel angle through the reference path and the real-time state of the vehicle, so as to realize the automatic steering of the vehicle and make the vehicle track the reference path stably and accurately. The aim of path tracking control is to minimize the lateral deviation and maintain an acceptable yaw rate. The existing research generally adopts three types of vehicle models to design the path tracking controller, which are based on the geometric model, kinematic model and dynamic model, respectively [2]. Among them, the representative algorithms based on the geometric model include the pure tracking algorithm and the Stanley algorithm. The application scenarios are relatively limited, which are mostly applicable to the path tracking control at low speed due to the control accuracy of the pure tracking algorithm depending on the selection of the forward-looking distance.