DocumentCode :
165329
Title :
Robust fault identification of a polytopic LPV system with neural network
Author :
Luzar, Marcel ; Witczak, Marcin ; Mrugalski, Marcin ; Kanski, Zbigniew
Author_Institution :
Inst. of Control & Comput. Eng., Univ. of Zielona Gor, Zielona Góra, Poland
fYear :
2014
fDate :
8-10 Oct. 2014
Firstpage :
1614
Lastpage :
1619
Abstract :
In this paper, a discrete-time Linear Parameter-Varying (LPV) system identification method using artificial neural network is described. In particular, neural network is transformed to obtain LPV model of the non-linear system. Moreover, a novel robust fault diagnosis scheme is developed, which is based on an observer within H framework for a class of non-linear systems. The effectiveness of the proposed approach is illustrated by the faults estimation in the multi-tank system.
Keywords :
H control; discrete time systems; neurocontrollers; nonlinear systems; robust control; time-varying systems; LPV model; LPV system identification method; artificial neural network; discrete-time linear parameter-varying system identification method; fault estimation; multitank system; nonlinear systems; polytopic LPV system; robust fault diagnosis scheme; robust fault identification; Computational modeling; Data models; Fault diagnosis; Neural networks; Observers; Robustness; Vectors;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Intelligent Control (ISIC), 2014 IEEE International Symposium on
Conference_Location :
Juan Les Pins
Type :
conf
DOI :
10.1109/ISIC.2014.6967628
Filename :
6967628
Link To Document :
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