Event-Triggered Adaptive Practical Fixed-Time Trajectory Tracking Control for Unmanned Surface Vehicle

This brief investigates the fixed-time trajectory tracking control problem for unmanned surface vehicle with unknown dynamics based on command filtered backstepping technique and fixed-time stability theory. Distinct from the existing results where the control execution is periodic and the computational burden is overlarge, a novel event-triggered adaptive practical fixed-time fuzzy controller is designed to guarantee the fixed-time stability of the closed-loop system (CLS), where the controller is aperiodically updated only at the event-sampled instants. Theoretical analysis proves that the tracking errors can diminish to an arbitrarily small neighborhood of the origin within a fixed time interval and the prescribed convergence time is free of the initial states of the surface vehicle under the proposed control method. Finally, the simulation results are provided to demonstrate the validity of the developed control approach.