Reinforcement learning control for underactuated surface vessel with output error constraints and uncertainties

This study investigates the trajectory tracking control problem of an underactuated marine vessel in the presence of output constraints, model uncertainties and environmental disturbances. The error transformation technique can ensure that the tracking errors remain within the predefined constraint boundaries. The controller is designed in combination with the critic function and the reinforcement learning (RL) algorithm based on actor-critic neural networks. The RL method is applied to solve model uncertainties and disturbances, and the critic function modifies the control action to supervise the system performance. Based on Lyapunov's direct method, a stability analysis is proposed to prove that the boundedness of system signals and the desired tracking performance can be guaranteed. Finally, the simulation illustrates the effectiveness and feasibility of the proposed controller.