Adaptive MPC path-tracking controller based on reinforcement learning and preview-based PID controller

Abstract

Path-tracking control is a crucial process for autonomous vehicles, ensuring that the vehicle drives safely along the reference path, and the suitable controller parameters ensure the accuracy and stability of this process. To enhance the adaptability of traditional path-tracking controller parameters, this paper proposes an adaptive model predictive control (MPC) controller based on a preview-based PID controller and deep deterministic policy gradient (DDPG) algorithm to achieve adaptive tuning of the controller parameters. Starting with the design of the dynamics tracking error model of the vehicle and the MPC controller. Based on the actor-critic reinforcement learning architecture, the DDPG agent is designed to tune the prediction horizon and weight matrix of the MPC controller. A preview-based PID controller is proposed to improve the efficiency and stability of reinforcement learning and compensate for the error in vehicle modeling. The improved algorithm performance is verified through the simulation scenarios of high-speed lane changing and accelerated overtaking scenarios constructed by MATLAB/Simulink. The results show that the improved algorithm significantly improves the adaptive ability of the traditional MPC controller to time-varying conditions and achieves higher tracking accuracy and stability.