Reinforcement learning-based optimal trajectory tracking control of surface vessels under input saturations

This paper develops a reinforcement learning (RL)-based optimal trajectory tracking control scheme of surface vessels with unknown dynamics, unknown disturbances, and input saturations of surface vessels. The control scheme is designed by combining the optimal control theory, adaptive neural networks, and the RL method in a unified actor-critic NN framework. A hyperbolic-type penalty function of the control input is designed so as to deal with the input saturations of surface vessels. An actor-critic NN-based RL mechanism is established to learn the optimal trajectory tracking control law without the knowledge of the surface vessel dynamics and disturbances, where NN weights are tuned online on the basis of devised tuning laws. Theoretical analysis and simulation results prove that the proposed RL-based optimal trajectory tracking control scheme can ensure surface vessels track the desired trajectory, while guaranteeing the boundedness of all signals in the surface vessel optimal trajectory tracking closed-loop control system.