Fixed Threshold Event-triggered Adaptive Sliding Mode Trajectory Tracking Control of Unmanned Surface Vehicles

This paper studies the trajectory tracking control of underactuated unmanned surface vehicles (USVs) with model uncertainties and environmental disturbances, and a novel fixed threshold event-triggered adaptive sliding mode control (FTETASMC) strategy. Firstly, the adaptive sliding mode control is used to design the virtual control law of the USV's surge velocity and sway velocity to achieve the convergence of the position tracking error. Then, considering the model uncertainties and environmental disturbances, the RBF neural network with the minimum learning parameter (RBFNN-MLP) and parameter adaptive update law is solved. Besides, the fixed threshold eventtriggered mechanism is introduced to update the controller, reducing the update frequency of the controller and the loss of the actuator. Stability analysis proves that the designed system is uniformly ultimately bounded. The simulation results verify the effectiveness of the proposed control strategy.