Fuzzy Event-Driven Optimal Quantized Control for USVs Under Thruster and Positioning Constraints: An Adaptive Dynamic Programming Method

This paper presents a fuzzy event-driven optimal quantized feedback control strategy for unmanned surface vehicle (USV) trajectory tracking under multiple practical constraints, including limited maritime communication resources, model uncertainties, thruster saturation, and positioning constraints. To address the communication limitation, a signal quantization and event-driven mechanism is introduced, where the thruster quantization is characterized by a linear analytical model. A fuzzy adaptive observer (FAO) is used to estimate the quantized feedback signal, while a finite-time disturbance observer (FDO) estimates environmental disturbances. Additionally, a dynamic auxiliary system compensates for thruster saturation effects. A logarithmic barrier function-based constraint mechanism ensures positioning variables remain within predefined safety bounds. The adaptive quantized feedback controller is designed using the backstepping framework and dynamic surface control techniques, with an optimal compensation term based on adaptive dynamic programming (ADP) to minimize the cost function of the tracking error system. The proposed control strategy ensures that all closed-loop signals remain within specified limits. Simulation results confirm the effectiveness and advantages of this approach.