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Computational Intelligence and Neuroscience
Volume 2016, Article ID 3013280, 11 pages
http://dx.doi.org/10.1155/2016/3013280
Research Article

Course Control of Underactuated Ship Based on Nonlinear Robust Neural Network Backstepping Method

College of Automation, Harbin Engineering University, Harbin 150001, China

Received 25 August 2015; Revised 25 November 2015; Accepted 31 January 2016

Academic Editor: Chaomin Luo

Copyright © 2016 Junjia Yuan et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

The problem of course control for underactuated surface ship is addressed in this paper. Firstly, neural networks are adopted to determine the parameters of the unknown part of ideal virtual backstepping control, even the weight values of neural network are updated by adaptive technique. Then uniform stability for the convergence of course tracking errors has been proven through Lyapunov stability theory. Finally, simulation experiments are carried out to illustrate the effectiveness of proposed control method.