Robust Adaptive Neural Backstepping Control for a Class of Nonlinear Systems with Dynamic Uncertainties
Joint Authors
Yang, Hongyan
Wang, Huanqing
Karimi, Hamid Reza
Source
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-12, 12 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-07-02
Country of Publication
Egypt
No. of Pages
12
Main Subjects
Abstract EN
This paper is concerned with adaptive neural control of nonlinear strict-feedback systems with nonlinear uncertainties, unmodeled dynamics, and dynamic disturbances.
To overcome the difficulty from the unmodeled dynamics, a dynamic signal is introduced.
Radical basis function (RBF) neural networks are employed to model the packaged unknown nonlinearities, and then an adaptive neural control approach is developed by using backstepping technique.
The proposed controller guarantees semiglobal boundedness of all the signals in the closed-loop systems.
A simulation example is given to show the effectiveness of the presented control scheme.
American Psychological Association (APA)
Yang, Hongyan& Wang, Huanqing& Karimi, Hamid Reza. 2014. Robust Adaptive Neural Backstepping Control for a Class of Nonlinear Systems with Dynamic Uncertainties. Abstract and Applied Analysis،Vol. 2014, no. 2014, pp.1-12.
https://search.emarefa.net/detail/BIM-1014537
Modern Language Association (MLA)
Yang, Hongyan…[et al.]. Robust Adaptive Neural Backstepping Control for a Class of Nonlinear Systems with Dynamic Uncertainties. Abstract and Applied Analysis No. 2014 (2014), pp.1-12.
https://search.emarefa.net/detail/BIM-1014537
American Medical Association (AMA)
Yang, Hongyan& Wang, Huanqing& Karimi, Hamid Reza. Robust Adaptive Neural Backstepping Control for a Class of Nonlinear Systems with Dynamic Uncertainties. Abstract and Applied Analysis. 2014. Vol. 2014, no. 2014, pp.1-12.
https://search.emarefa.net/detail/BIM-1014537
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1014537