Neural adaptive steering of an unmanned surface vehicle with measurement noises

This paper presents an autopilot design for a robotic unmanned surface vehicle in the presence of unknown yaw dynamics and measurement noises. A robust adaptive steering law is developed with, the aid of a predictor, neural networks, and a modified dynamic surface control technique. Specifically, a predictor together with a low-frequency learning-based neural updating law is developed to identify the unknown yaw dynamics, as well as to reconstruct the states corrupted by measurement noises. Besides, to avoid the noise amplification effect of first-order filter, a linear tracldng differentiator is incorporated into the dynamic surface control design approach to produce a noise-tolerant estimate of virtual control derivative. The stability of the closed-loop autopilot system is established by Lyapunov analysis. A salient feature of the developed controller is that it can achieve the desired performance in the presence of model uncertainty and measurement noises, simultaneously. Both simulation and experiment results are given to validate the efficacy of the proposed method. (C) 2016 Elsevier B.V. All rights reserved.