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

Joint Authors

Zhou, Jiajia
Yuan, Junjia
Meng, Hao
Zhu, Qidan

Source

Computational Intelligence and Neuroscience

Issue

Vol. 2016, Issue 2016 (31 Dec. 2015), pp.1-11, 11 p.

Publisher

Hindawi Publishing Corporation

Publication Date

2016-02-24

Country of Publication

Egypt

No. of Pages

11

Main Subjects

Biology

Abstract EN

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.

American Psychological Association (APA)

Yuan, Junjia& Meng, Hao& Zhu, Qidan& Zhou, Jiajia. 2016. Course Control of Underactuated Ship Based on Nonlinear Robust Neural Network Backstepping Method. Computational Intelligence and Neuroscience،Vol. 2016, no. 2016, pp.1-11.
https://search.emarefa.net/detail/BIM-1099625

Modern Language Association (MLA)

Yuan, Junjia…[et al.]. Course Control of Underactuated Ship Based on Nonlinear Robust Neural Network Backstepping Method. Computational Intelligence and Neuroscience Vol. 2016, no. 2016 (2015), pp.1-11.
https://search.emarefa.net/detail/BIM-1099625

American Medical Association (AMA)

Yuan, Junjia& Meng, Hao& Zhu, Qidan& Zhou, Jiajia. Course Control of Underactuated Ship Based on Nonlinear Robust Neural Network Backstepping Method. Computational Intelligence and Neuroscience. 2016. Vol. 2016, no. 2016, pp.1-11.
https://search.emarefa.net/detail/BIM-1099625

Data Type

Journal Articles

Language

English

Notes

Includes bibliographical references

Record ID

BIM-1099625