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A Neuro-Augmented Observer for Robust Fault Detection in Nonlinear Systems
Joint Authors
Source
Mathematical Problems in Engineering
Issue
Vol. 2012, Issue 2012 (31 Dec. 2012), pp.1-8, 8 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2012-12-03
Country of Publication
Egypt
No. of Pages
8
Main Subjects
Abstract EN
A new fault detection method using neural-networks-augmented state observer for nonlinear systems is presented in this paper.
The novelty of the approach is that instead of approximating the entire nonlinear system with neural network, we only approximate the unmodeled part that is left over after linearization, in which a radial basis function (RBF) neural network is adopted.
Compared with conventional linear observer, the proposed observer structure provides more accurate estimation of the system state.
The state estimation error is proved to asymptotically approach zero by the Lyapunov method.
An aircraft system demonstrates the efficiency of the proposed fault detection scheme, simulation results of which show that the proposed RBF neural network-based observer scheme is effective and has a potential application in fault detection and identification (FDI) for nonlinear systems.
American Psychological Association (APA)
Gong, Huajun& Zhen, Ziyang. 2012. A Neuro-Augmented Observer for Robust Fault Detection in Nonlinear Systems. Mathematical Problems in Engineering،Vol. 2012, no. 2012, pp.1-8.
https://search.emarefa.net/detail/BIM-1001919
Modern Language Association (MLA)
Gong, Huajun& Zhen, Ziyang. A Neuro-Augmented Observer for Robust Fault Detection in Nonlinear Systems. Mathematical Problems in Engineering No. 2012 (2012), pp.1-8.
https://search.emarefa.net/detail/BIM-1001919
American Medical Association (AMA)
Gong, Huajun& Zhen, Ziyang. A Neuro-Augmented Observer for Robust Fault Detection in Nonlinear Systems. Mathematical Problems in Engineering. 2012. Vol. 2012, no. 2012, pp.1-8.
https://search.emarefa.net/detail/BIM-1001919
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1001919