Title :
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
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;
Conference_Titel :
Decision and Control, 1999. Proceedings of the 38th IEEE Conference on
Conference_Location :
Phoenix, AZ
Print_ISBN :
0-7803-5250-5
DOI :
10.1109/CDC.1999.831416
