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