Reinforcement Learning-Based Robust Vehicle Control for Autonomous Vehicle Trajectory Tracking

This publication presents a new method by which control methods based on reinforcement learning can be combined with classical robust control methods. The combination results in a robust management system that meets high-quality criteria. The described method is presented through the control of an autonomous vehicle. By choosing the reward function chosen during reinforcement learning, various driving styles can be realized, e.g., lap time minimization, track tracking, and travel comfort. The neural network was trained using the Proximal Policy Optimization algorithm, and the robust control is based on H∞. The two controllers are combined using a supervisor structure, in which a quadratic optimization task is implemented. The result of the method is a control structure that realizes the longitudinal and lateral control of the vehicle by specifying the reference speed and the steering angle. The effectiveness of the algorithm is demonstrated through simulations.