A novel path tracking system for autonomous vehicle based on model predictive control

Aiming to promote the control performance of traditional fixed-parameter model predictive control (MPC), a new longitudinal and lateral coupling path tracking control method for the autonomous vehicle is proposed. First, a dynamic model of the autonomous vehicle, which can describe the longitudinal and lateral motion states, is established. Secondly, both the tire side-slip angle and centroid side-slip angle constraints are introduced in the path tracking controller design, which improves the accuracy of the prediction model at high speed. Furthermore, the key parameters of the proposed path tracking controller are optimized. The simulation and experimental results show that compared with the traditional MPC tracking control method, the lateral error and directional error of the proposed optimized path tracking control method under high speed decrease by 19.17 % and 11.89 %, respectively, which improves the path tracking performance significantly.