Event-triggered robust nonsingular predefined-time sliding mode control for underactuated USVs

This paper addresses the adaptive predefined-time trajectory tracking problem for underactuated unmanned surface vehicles (USVs), with the incorporation of an event-triggered control mechanism. First, a predefined-time sliding mode control law is designed to guarantee the predefined convergence. Additionally, an event-triggered mechanism is introduced to reduce the computational load and communication burden while maintaining predefined-time convergence for the underactuated system. Meanwhile, the radial basis function neural network method is employed to approximate unknown disturbances and modeling uncertainties. Furthermore, a rigorous proof demonstrates that the proposed control method guarantees the boundedness of all states in the closed-loop system, ensures that the tracking error signals converge to a neighborhood of the origin within a predefined time, and prevents the occurrence of the Zeno phenomenon. Numerical simulations validate the effectiveness and robustness of the proposed method, confirming its feasibility for practical applications.