Signal Quantization and Event‐Triggered Trajectory Tracking for Multi‐Constraint Control of Unmanned Surface Vehicle

ABSTRACT This paper addresses the adaptive trajectory tracking control problem for unmanned surface vehicle (USV) operating under challenging conditions, including thruster saturation, unknown dynamics, state constraints, and unavailable velocity measurements. An adaptive quantized feedback control strategy based on a neural network observer is proposed. To address the challenge of limited maritime communication resources, a signal quantization and event‐triggered mechanism was introduced. The input quantization process is described by a linear analytical model. A neural network observer is designed to estimate the unavailable velocity states. Neural networks are employed to approximate the unknown system dynamics, and a low‐frequency gain learning method is introduced to effectively suppress control signal chattering induced by external disturbances. A dynamic auxiliary system is designed to compensate for the effects of thruster saturation. Furthermore, a constraint‐handling mechanism based on a logarithmic barrier function is incorporated to ensure that all state variables remain strictly within the prescribed safe boundaries. Rigorous stability analysis of the resulting closed‐loop system is conducted based on Lyapunov stability theory. Comprehensive simulation studies validate the effectiveness of the proposed control strategy.