Adaptive Neural Network Sliding Mode Control for an Unmanned Surface Vessels

Autonomous vessels have been given interesting research topics in both the maritime area and the control engineering field with increasingly numerous applications. The practical system is impacted by a vast range of uncertainties, including the high nonlinearities of the dynamic model, the variable working environments, and the influences of ocean currents, waves, and wind. These factors give the big challenges for control, especially precise trajectory tracking control. In this research, a new stage adaptive neural network back-stepping control based on sliding-mode control for the Fossen's ship model is proposed. By using the artificial neural network to approximate the composite time-varying disturbance function, the controller can solve the problems of accurate control tracking the displacement of the surface vessel following the convergence of the Lyapunov function. The numerical simulation results illustrate the effectiveness of the designed control law.