Chaos Synchronization Based on Unknown Input Proportional Multiple-Integral Fuzzy Observer

This paper presents an unknown input Proportional Multiple-Integral Observer (PIO) for synchronization of chaotic systems based on Takagi-Sugeno (TS) fuzzy chaotic models subject to unmeasurable decision variables and unknown input. In a secure communication configuration, this unknown input is rega...

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Main Authors: T. Youssef, M. Chadli, H. R. Karimi, M. Zelmat
Format: Article
Language:English
Published: Hindawi Limited 2013-01-01
Series:Abstract and Applied Analysis
Online Access:http://dx.doi.org/10.1155/2013/670878
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spelling doaj-bdf42aa0ff2649a6b58ea6e00a2df0152020-11-24T23:48:41ZengHindawi LimitedAbstract and Applied Analysis1085-33751687-04092013-01-01201310.1155/2013/670878670878Chaos Synchronization Based on Unknown Input Proportional Multiple-Integral Fuzzy ObserverT. Youssef0M. Chadli1H. R. Karimi2M. Zelmat3Laboratory of Modeling, Information & Systems (MIS), University of Picardie Jules Verne (UPJV), 33 rue Saint Leu, 80039 Amiens Cedex 1, FranceLaboratory of Modeling, Information & Systems (MIS), University of Picardie Jules Verne (UPJV), 33 rue Saint Leu, 80039 Amiens Cedex 1, FranceDepartment of Engineering, Faculty of Engineering and Science, University of Agder, 4898 Grimstad, NorwayLaboratory of Automatic Applied (LAA), M’hamed Bougara University of Boumerdès (UMBB), 35000 Boumerdès, AlgeriaThis paper presents an unknown input Proportional Multiple-Integral Observer (PIO) for synchronization of chaotic systems based on Takagi-Sugeno (TS) fuzzy chaotic models subject to unmeasurable decision variables and unknown input. In a secure communication configuration, this unknown input is regarded as a message encoded in the chaotic system and recovered by the proposed PIO. Both states and outputs of the fuzzy chaotic models are subject to polynomial unknown input with kth derivative zero. Using Lyapunov stability theory, sufficient design conditions for synchronization are proposed. The PIO gains matrices are obtained by resolving linear matrix inequalities (LMIs) constraints. Simulation results show through two TS fuzzy chaotic models the validity of the proposed method.http://dx.doi.org/10.1155/2013/670878
collection DOAJ
language English
format Article
sources DOAJ
author T. Youssef
M. Chadli
H. R. Karimi
M. Zelmat
spellingShingle T. Youssef
M. Chadli
H. R. Karimi
M. Zelmat
Chaos Synchronization Based on Unknown Input Proportional Multiple-Integral Fuzzy Observer
Abstract and Applied Analysis
author_facet T. Youssef
M. Chadli
H. R. Karimi
M. Zelmat
author_sort T. Youssef
title Chaos Synchronization Based on Unknown Input Proportional Multiple-Integral Fuzzy Observer
title_short Chaos Synchronization Based on Unknown Input Proportional Multiple-Integral Fuzzy Observer
title_full Chaos Synchronization Based on Unknown Input Proportional Multiple-Integral Fuzzy Observer
title_fullStr Chaos Synchronization Based on Unknown Input Proportional Multiple-Integral Fuzzy Observer
title_full_unstemmed Chaos Synchronization Based on Unknown Input Proportional Multiple-Integral Fuzzy Observer
title_sort chaos synchronization based on unknown input proportional multiple-integral fuzzy observer
publisher Hindawi Limited
series Abstract and Applied Analysis
issn 1085-3375
1687-0409
publishDate 2013-01-01
description This paper presents an unknown input Proportional Multiple-Integral Observer (PIO) for synchronization of chaotic systems based on Takagi-Sugeno (TS) fuzzy chaotic models subject to unmeasurable decision variables and unknown input. In a secure communication configuration, this unknown input is regarded as a message encoded in the chaotic system and recovered by the proposed PIO. Both states and outputs of the fuzzy chaotic models are subject to polynomial unknown input with kth derivative zero. Using Lyapunov stability theory, sufficient design conditions for synchronization are proposed. The PIO gains matrices are obtained by resolving linear matrix inequalities (LMIs) constraints. Simulation results show through two TS fuzzy chaotic models the validity of the proposed method.
url http://dx.doi.org/10.1155/2013/670878
work_keys_str_mv AT tyoussef chaossynchronizationbasedonunknowninputproportionalmultipleintegralfuzzyobserver
AT mchadli chaossynchronizationbasedonunknowninputproportionalmultipleintegralfuzzyobserver
AT hrkarimi chaossynchronizationbasedonunknowninputproportionalmultipleintegralfuzzyobserver
AT mzelmat chaossynchronizationbasedonunknowninputproportionalmultipleintegralfuzzyobserver
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