title: Neural network-based robust actuator fault diagnosis for a non-linear multi-tank system.
abstract: The paper is devoted to the problem of the robust actuator fault diagnosis of the dynamic non-linear systems. In the proposed method, it is assumed that the diagnosed system can be modelled by the recurrent neural network, which can be transformed into the linear parameter varying form. Such a system description allows developing the designing scheme of the robust unknown input observer within H∞ framework for a class of non-linear systems. The proposed approach is designed in such a way that a prescribed disturbance attenuation level is achieved with respect to the actuator fault estimation error, while guaranteeing the convergence of the observer. The application of the robust unknown input observer enables actuator fault estimation, which allows applying the developed approach to the fault tolerant control tasks.
authors.affiliation: Institute of Control and Computation Engineering, University of Zielona Góra, ul. Podgórna 50, 65-246 Zielona Góra, Poland. Electronic address: M.Mrugalski@issi.uz.zgora.pl.
authors.forename: Marcin
authors.initials: M
authors.lastname: Mrugalski
authors.affiliation: Institute of Control and Computation Engineering, University of Zielona Góra, ul. Podgórna 50, 65-246 Zielona Góra, Poland. Electronic address: M.Luzar@issi.uz.zgora.pl.
authors.forename: Marcel
authors.initials: M
authors.lastname: Luzar
authors.affiliation: Institute of Control and Computation Engineering, University of Zielona Góra, ul. Podgórna 50, 65-246 Zielona Góra, Poland. Electronic address: marcin.pazera@o2.pl.
authors.forename: Marcin
authors.initials: M
authors.lastname: Pazera
authors.affiliation: Institute of Control and Computation Engineering, University of Zielona Góra, ul. Podgórna 50, 65-246 Zielona Góra, Poland. Electronic address: M.Witczak@issi.uz.zgora.pl.
authors.forename: Marcin
authors.initials: M
authors.lastname: Witczak
authors.affiliation: Centre de Recherche en Automatique de Nancy, CRAN-UMR 7039, Nancy-Universite, CNRS, F-54506 Vandoeuvre-les-Nancy Cedex, France. Electronic address: christophe.aubrun@univ-lorraine.fr.
authors.forename: Christophe
authors.initials: C
authors.lastname: Aubrun
keywords: Fault estimation; LPV systems; Neural network; Non-linear systems identification; Observers; Robust fault diagnosis