Event-Triggered Optimal Path-Following Control for Wind-Assisted Autonomous Surface Vehicles via Actor–Critic Reinforcement Learning

This paper proposes an enhanced event-triggered optimal control scheme integrated with reinforcement learning (RL) for wind-assisted autonomous surface vehicles (WAASVs), aiming to ensure the safety and energy efficiency of marine path-following missions. To address the uncertainties arising from the dynamic model and time-varying external disturbances, a reinforcement learning approach based on the architecture of actor–critic neural networks (AC-NNs) is employed to generate control signals without relying on precise model knowledge while minimizing path deviation and energy consumption. Furthermore, an integral event-triggered control (IETC) algorithm is developed to dynamically adjust the control signal updates according to the system output errors, which offers a promising solution to prevent excessive mechanical wear of the actuators. The stability of all error variables is rigorously analyzed using the Lyapunov theory. Finally, two simulation experiments on the rotor-assisted vehicle are performed to validate the superior tracking performance and practical applicability of the proposed algorithm.