Fuzzy course tracking control of an unmanned surface vehicle with event-triggered mechanism and input quantization

This paper considers the course tracking control of an unmanned surface vehicle with event-triggered mechanism and input quantization, aiming to reduce the influence of external interference and saving communication resources. The fuzzy logic system is used to approximate the model uncertainty and external interference. A linear analytical model is used to describe the input quantization process. The adaptive fuzzy quantization controller does not need the prior information of quantization parameters. Then an event-triggered adaptive fuzzy quantization control law is proposed. In this way, the frequency and amplitude of execution are reduced and the communication burden is relaxed. On the basis of the Lyapunov stability theory, the stability of the proposed control structure is proved, and all the signals in the closed-loop system are ultimately bounded. Finally, the effectiveness and feasibility of the proposed algorithm are verified by simulation experiments.