Path Tracking Control of Autonomous Ground Vehicles Via Model Predictive Control and Deep Deterministic Policy Gradient Algorithm

The automated steering controller is crucial for smooth and accurate path tracking of autonomous ground vehicles (AGVs). However, time-varying uncertainties and disturbances may deteriorate the path tracking performance. Moreover, it is difficult for the steering system to strictly follow the desired steering angle in practice. Therefore, this paper proposes an automated steering control algorithm consisting of two parts: 1) an output feedback model predictive controller (MPC) to solve the path tracking problem, which is formulated as an optimization problem in this paper, with strong robustness against time-varying uncertainty and disturbance; 2) a feedforward compensator for the steering angle calculated by MPC using deep deterministic policy gradient (DDPG) algorithm so that the steering system can execute the desired steering angle more quickly and more accurately. Simulation results demonstrate that the proposed control scheme can significantly improve response speed and accuracy for path tracking of AGVs with strong robustness.