Autopilot design for a robotic unmanned surface vehicle

Abstract

This paper reports an autopilot design for a robotic unmanned surface vehicle in the control laboratory at DMU. A robust adaptive steering law is developed with the aid of a predictor, a tracking differentiator, neural networks, and a dynamic surface control technique. The developed controller is able to achieve satisfactory performance in the presence of model uncertainties, time-varying ocean disturbances, and measurement noises. Simulation results demonstrate the efficacy of the proposed method.