Fuzzy Reinforcement Learning Disturbance Cancellation Optimized Course Tracking Control for USV Autopilot Under Actuator Constraint

Unmanned surface vehicles (USVs) course control research constitutes a vital branch of ship motion control studies and serves as a key technology for the development of marine critical equipment. Aiming at the problems of model uncertainties, external marine disturbances, performance optimization, and actuator constraints encountered by the autopilot system, this paper proposes a composite disturbance cancellation optimized control method based on fuzzy reinforcement learning. Firstly, a coupling design of the finite-time disturbance observer and fuzzy logic system is conducted to estimate and reject the composite disturbance composed of internal model uncertainty and ocean disturbances. Secondly, a modified backstepping control technique is employed to design the autopilot controller and construct the error system. Based on the designed performance index function, the fuzzy reinforcement learning is utilized to propose an optimized compensation term for the error system. Meanwhile, to address the actuator saturation issue, an auxiliary system is introduced to modify the error surface, reducing the impact of saturation on the system. Finally, the stability of the autopilot system is proved using the Lyapunov stability theory. Simulation studies conducted on the ocean-going training ship “Yulong” demonstrate the effectiveness of the proposed algorithm. Under the strong and weak ocean conditions designed, this algorithm can ensure that the tracking error converges within 7 s.