Event-Triggered Optimal Tracking Control for Underactuated Surface Vessels via Neural Reinforcement Learning

This article presents a prescribed-time tracking control method for underactuated unmanned surface vessels (USVs) using a neural reinforcement learning (RL) approach. First, the hand position approach, addressing the underactuated characteristic, is employed to convert the model of USV into the integral cascade form. Second, inheriting the advantages of prescribed performance control (PPC), the proposed controller not only stabilizes the tracking error within an asymmetric prescribed-time range, but also removes the limitation of initial conditions. Subsequently, the identifier-actor-critic architecture is introduced in the optimized backstepping design, which gives the solution of the Hamilton-Jacobi-Bellman (HJB) equation. Meanwhile, the relative threshold event-triggered mechanism is also considered to reduce the communication burden and executive frequency of actuators. Finally, employing the Lyapunov stability theory, it is proven that all signals in the closed-loop system are bounded, and the developed control scheme is demonstrated to be effective through simulation and experimental results.