Event-triggered adaptive fuzzy trajectory tracking control of unmanned surface vehicle with time-varying output...

This paper proposes a novel event-triggered adaptive fuzzy control approach to tackle the trajectory tracking control problem of an unmanned surface vehicle (USV) under time-varying output constraints and input quantization effects. To address the challenges posed by model uncertainties, a fuzzy logic system (FLS) is employed for effective function approximation. An asymmetric Barrier Lyapunov Function (BLF) is incorporated into the controller to rigorously guarantee compliance with output constraints. Meanwhile, an event-triggered mechanism is developed to reduce communication overhead by avoiding unnecessary transmissions of control commands. To further reduce communication overhead, a uniform quantizer is adopted to manage input quantization, and a linear analytical model is introduced to characterize the input quantization process, thus facilitating controller design without requiring prior knowledge of quantizer parameters. The feasibility and effectiveness of the proposed control approach are demonstrated through rigorous stability analysis and comprehensive simulation studies, ensuring that the desired tracking accuracy is achieved.