Cooperative Quantized Event-Based Fuzzy Tracking Control of Nonlinear Autonomous Surface Vehicles With Prescribed Performance

This paper investigates the cooperative fuzzy tracking control of nonlinear autonomous surface vehicles with input quantization and event-triggered mechanism. The proposed cooperative control scheme consists of two parts: (i) the distributed observer and (ii) the dynamic event-based fuzzy tracking controller. The distributed observer is designed to obtain the nonlinear leader’s trajectory information on a directed communication topology. Under this framework, uncertain nonlinearity within the vehicle model is approximated through fuzzy logic systems, and, according to the state of the distributed observer, the dynamic event-based adaptive fuzzy tracking control law is developed with an input switching quantizer. Furthermore, a prescribed performance method is introduced to ensure the transient performance of tracking errors and obtain zero-tracking errors ultimately, which is proved through Lyapunov stability theory. Finally, the effectiveness of the proposed control strategy is verified by simulation experiments. Note to Practitioners—In marine operations, autonomous surface vehicles face challenges including limited network resources and model nonlinearity. This paper proposes a tracking control strategy to address these issues by integrating switching quantization and dynamic event-triggered mechanism for control input signals, which can reduce communication burden while avoiding Zeno behavior, with prescribed performance introduced to ensure transient performance. For the nonlinear and uncertain aspects of actual autonomous surface vehicle models, fuzzy logic systems are employed to approximate the nonlinear components. Additionally, in tracking control scenarios with nonlinear leader trajectory, a distributed observer is proposed to obtain the nonlinear trajectory information. The proposed method can ensure zero-tracking errors, achieving more precise tracking control under the complex nonlinearity, uncertainty, and limited network resources encountered in real-world scenarios.