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
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;
Conference_Titel :
Intelligent Control (ISIC), 2014 IEEE International Symposium on
Conference_Location :
Juan Les Pins
DOI :
10.1109/ISIC.2014.6967628