Adaptive Event-Triggered Dynamic Positioning Control for Fuzzy-Model-Based Unmanned Surface Vehicles

This paper considers the dynamic positioning control problem of unmanned surface vehicles (USVs) under the condition of limited communication resources. By analyzing the dynamic model of USVs, a Takagi-Sugeno (T-S) fuzzy model is established. Then, an optimized fuzzy-related adaptive event-triggered mechanism (FRAETM) is proposed, which considers subsystem structures and membership functions, offering more design flexibility than traditional methods. A novel Lyapunov-Krasovskii functional (LKF) is constructed using the two-side looped-functional, which contributes to relaxing the LKF constraints, fully utilizing the system states, and providing better stability criteria. Furthermore, the control gain matrix and the corresponding weight matrix of the system can be obtained. Finally, a simulation is given to demonstrate the effectiveness of the method and show its superiority in resource-saving and communication reduction.