Robust fault detection for nonlinear discrete-time Markovian jump systems with partly unknown transition probabilities

© 2016 IEEE.The problem of robust fault detection (RFD) for nonlinear discrete-time Markovian jump systems (MJSs) with partly unknown transition probabilities is investigated in the paper. With the method of T-S fuzzy linearization, the original systems are described as a set of local linear models....

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Main Authors: Shi, J., Yin, YanYan, Liu, F.
Format: Conference Paper
Published: 2016
Online Access:http://hdl.handle.net/20.500.11937/52152
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author Shi, J.
Yin, YanYan
Liu, F.
author_facet Shi, J.
Yin, YanYan
Liu, F.
author_sort Shi, J.
building Curtin Institutional Repository
collection Online Access
description © 2016 IEEE.The problem of robust fault detection (RFD) for nonlinear discrete-time Markovian jump systems (MJSs) with partly unknown transition probabilities is investigated in the paper. With the method of T-S fuzzy linearization, the original systems are described as a set of local linear models. The RFD observer (RFDO) system and the dynamics of error generator are constructed. By introducing some free-weighting matrices, the proposed method leads to less conservatism compared with the existing ones. Moreover, the H8 performance index is proposed to minimize the influence of the unknown disturbances. A sufficient condition is first established on the stochastic stability using stochastic Lyapunov-krasovskii function, then in the terms of linear matrix inequalities techniques, the sufficient conditions on the existence of RFDO are presented and proved. Finally, A simulation example is given to illustrate that the proposed RFDO can detect the faults correctly and shortly after the occurrence.
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institution Curtin University Malaysia
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spelling curtin-20.500.11937-521522017-09-13T15:39:04Z Robust fault detection for nonlinear discrete-time Markovian jump systems with partly unknown transition probabilities Shi, J. Yin, YanYan Liu, F. © 2016 IEEE.The problem of robust fault detection (RFD) for nonlinear discrete-time Markovian jump systems (MJSs) with partly unknown transition probabilities is investigated in the paper. With the method of T-S fuzzy linearization, the original systems are described as a set of local linear models. The RFD observer (RFDO) system and the dynamics of error generator are constructed. By introducing some free-weighting matrices, the proposed method leads to less conservatism compared with the existing ones. Moreover, the H8 performance index is proposed to minimize the influence of the unknown disturbances. A sufficient condition is first established on the stochastic stability using stochastic Lyapunov-krasovskii function, then in the terms of linear matrix inequalities techniques, the sufficient conditions on the existence of RFDO are presented and proved. Finally, A simulation example is given to illustrate that the proposed RFDO can detect the faults correctly and shortly after the occurrence. 2016 Conference Paper http://hdl.handle.net/20.500.11937/52152 10.1109/WCICA.2016.7578334 restricted
spellingShingle Shi, J.
Yin, YanYan
Liu, F.
Robust fault detection for nonlinear discrete-time Markovian jump systems with partly unknown transition probabilities
title Robust fault detection for nonlinear discrete-time Markovian jump systems with partly unknown transition probabilities
title_full Robust fault detection for nonlinear discrete-time Markovian jump systems with partly unknown transition probabilities
title_fullStr Robust fault detection for nonlinear discrete-time Markovian jump systems with partly unknown transition probabilities
title_full_unstemmed Robust fault detection for nonlinear discrete-time Markovian jump systems with partly unknown transition probabilities
title_short Robust fault detection for nonlinear discrete-time Markovian jump systems with partly unknown transition probabilities
title_sort robust fault detection for nonlinear discrete-time markovian jump systems with partly unknown transition probabilities
url http://hdl.handle.net/20.500.11937/52152