Dynamic event-triggered integral non-singular terminal sliding mode tracking of unmanned surface vehicle via an event-triggered extended state observer and adaptive neural network

This paper presents a novel trajectory tracking controller for an underactuated unmanned surface vehicle (USV). The controller incorporates an event-triggered extended state observer (ETESO), a minimum learning parameter neural network, an integral non-singular terminal sliding mode (INTSM) control strategy, and a dynamic event-triggered mechanism (DETM). Firstly, an ETESO is developed to estimate unmeasurable velocities and lumped disturbances, differentiating it from most existing extended state observers without the necessity for real-time output measurements. To further alleviate the communication burden and minimize actuator wear, a DETM with an adjustable threshold is introduced. In contrast to traditional event-triggered methods, which employ fixed threshold parameters, this mechanism allows for online adaptive updates of the triggering thresholds, thereby enhancing resource efficiency. Additionally, an INTSM is designed to ensure rapid convergence of the position and velocity errors of the USV. To effectively counteract external disturbances and internal modeling uncertainties, a minimum learning parameter (MLP) neural network algorithm is implemented to approximate and compensate for these uncertainties. Finally, using Lyapunov's theory, it is demonstrated that all signals within the closed-loop tracking control system remain bounded. Simulation results are given to illustrate the effectiveness of theoretical results.