Optimal Motion Design for Autonomous Vehicles with Learning Aided Robust Control

Abstract

This paper presents a control design framework for the integration of robust controller and reinforcement learning-based (RL) control agent. The proposed integration method is applied to motion control of autonomous road vehicles, providing safe motion. The RL-based control agent is used to determine the steering angle and reference velocity of the vehicle to achieve high-performance motion. The chosen reward function is used to achieve different driving behaviors, e.g. high-velocity motion with minimal lap time, path following, or the limitation of control energy. The RL-based control through Proximal Policy Optimization method during episodes is performed. Safe motion is achieved by using a supervisory control framework which is based on the robust mathcal {H}-infty control method, and able to keep limits on lateral path tracking error. The effectiveness of the proposed control through simulation examples with comparisons to predictive control methods is illustrated. Moreover, the applicability of the method through a real-life test scenario on a small-scaled test vehicle is demonstrated.