Event-Based Robust Reinforcement Learning Course Tracking for MSVs with Actuator Faults

This paper investigates the robust course control for marine surface vehicles (MSVs) by using reinforcement learning and a dynamic event-triggered mechanism under rudder faults. A fault-tolerant optimized tracking control scheme is designed based on reinforcement learning. Firstly, a general actuator fault model is established. Secondly, internal and external disturbances as well as fault dynamics are aggregated into lumped uncertainties, and a fault observer is constructed to estimate and compensate the lumped uncertainty term. Then, a tracking cost function for the MSV system is constructed, and a reinforcement learning framework with a single critic network is used to approximate the value function and generate a learning-based compensation input. Finally, a dynamic event-triggered mechanism is implemented between the control layer and the execution layer to reduce the triggering frequency of actuators. Lyapunov stability theory is utilized to prove that all signals within the closed-loop system are uniformly ultimately bounded. Simulation results verify that the proposed control scheme achieves favorable overall performance.