Event‐Triggered Fuzzy Course‐Tracking Control of Unmanned Surface Vehicle With Input and State Quantization

ABSTRACT

This article investigates the course‐tracking control problem of an unmanned surface vehicle (USV) subject to dynamic input and state quantization under an event‐triggered framework. Time‐varying scaling parameters are introduced into the dynamic quantizers to adjust the quantization precision online. The controller is developed based on quantized state signals, and the actual control input is updated through an event‐triggered mechanism using the dynamically quantized input. By combining backstepping with dynamic surface control, the control laws are systematically designed while reducing the computational burden caused by repeated differentiation of virtual control laws. A fuzzy logic system is employed to approximate the unknown nonlinear dynamics. Furthermore, an event‐triggered adaptive fuzzy quantized control strategy is proposed to reduce the control update frequency, thereby reducing the frequency of actuator updates and the associated communication load. Lyapunov‐based stability analysis proves that all closed‐loop signals of the resulting system are bounded. Finally, simulation results demonstrate the effectiveness of the proposed control scheme.