Fault estimation for a class of nonlinear dynamical systems

Author

Wang, Y. ; Chan, C.W. ; Cheung, K.C. ; Chan, W.C.

Author_Institution

Dept. of Mech. Eng., Hong Kong Univ., Hong Kong

Volume

3

fYear

1999

fDate

1999

Firstpage

3128

Abstract

Model based fault estimation for a class of nonlinear dynamical systems is investigated. The state of the system is assumed unavailable, and a nonlinear observer is used to estimate the state. In the observer, a neurofuzzy network is used as the approximator to estimate faults. The network is trained online and the convergence of the proposed learning algorithm is established. Abrupt faults and incipient faults are analyzed in the paper and they can be estimated accurately using a neurofuzzy network with the proposed learning algorithm

Keywords

convergence; fault diagnosis; fuzzy neural nets; learning (artificial intelligence); monitoring; nonlinear dynamical systems; observers; recurrent neural nets; abrupt faults; incipient faults; learning algorithm; model based fault estimation; neurofuzzy network; nonlinear observer; Algorithm design and analysis; Convergence; Fault diagnosis; Mechanical engineering; Nonlinear dynamical systems; Nonlinear equations; Nonlinear systems; Observers; State estimation; Uncertainty;

fLanguage

English

Publisher

ieee

Conference_Titel

Decision and Control, 1999. Proceedings of the 38th IEEE Conference on

Conference_Location

Phoenix, AZ

ISSN

0191-2216

Print_ISBN

0-7803-5250-5

Type

conf

DOI

10.1109/CDC.1999.831416

Filename

831416