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The current paper verifies the event-triggered finite-time tracking control of a fully actuated unmanned surface vessel under unmodeled dynamics and external disturbances. Radial basis function neural networks (RBFNNs), nonlinear disturbance observers (NDO), and the event-triggered mechanism (ETM) are utilized to design a new type of finite time tracking controller (FFTC). The proposed controller utilizes the dynamic surface control (DSC) approach to resolve the &#x201C;differential explosion&#x201D; issue of virtual control laws. Prescribed performance functions (PPFs) and the finite-time control (FFC) technique are utilized to specify the efficiency of tracking errors. The designed control laws make the control system of USV semi-globally practically finite-time stable (SGPFS) and make the tracking errors tend to a narrow residual set involving the specified bound in a finite time. In the end, the simulations reflect the presented FFTC&#x2019;s efficiency.

Event-Triggered Finite-Time Tracking Control of Unmanned Surface Vessel Using Neural Network Prescribed Performance