Adaptive control of uncertain chaotic systems based on Takagi-Sugeno fuzzy models

C. W. Park; C. H. Lee; J. H. Kim; M. Park

Adaptive control of uncertain chaotic systems based on Takagi-Sugeno fuzzy models

C. W. Park, C. H. Lee, J. H. Kim, M. Park

Department of Electrical and Electronic Engineering

Research output: Contribution to journal › Article › peer-review

Abstract

In this paper, in order to control uncertain chaotic system, an adaptive fuzzy control (AFC) scheme is developed for the multi-input/multi-output plants represented by the Takagi-Sugeno (T-S) fuzzy models. The proposed AFC scheme provides robust tracking of a desired signal for the T-S fuzzy systems with uncertain parameters. The developed control law and adaptive law guarantee the boundedness of all signals in the closed-loop system. In addition, the chaotic state tracks the state of the stable reference model (SRM) asymptotically with time for any bounded reference input signal. The suggested AFC design technique is applied to control of a uncertain Lorenz system based on T-S fuzzy model such os stabilization, synchronization and chaotic model following control (CMFC).

Original language	English
Pages (from-to)	2101-2117
Number of pages	17
Journal	IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
Volume	E84-A
Issue number	9
Publication status	Published - 2001 Sep

All Science Journal Classification (ASJC) codes

Signal Processing
Computer Graphics and Computer-Aided Design
Electrical and Electronic Engineering
Applied Mathematics

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title = "Adaptive control of uncertain chaotic systems based on Takagi-Sugeno fuzzy models",

abstract = "In this paper, in order to control uncertain chaotic system, an adaptive fuzzy control (AFC) scheme is developed for the multi-input/multi-output plants represented by the Takagi-Sugeno (T-S) fuzzy models. The proposed AFC scheme provides robust tracking of a desired signal for the T-S fuzzy systems with uncertain parameters. The developed control law and adaptive law guarantee the boundedness of all signals in the closed-loop system. In addition, the chaotic state tracks the state of the stable reference model (SRM) asymptotically with time for any bounded reference input signal. The suggested AFC design technique is applied to control of a uncertain Lorenz system based on T-S fuzzy model such os stabilization, synchronization and chaotic model following control (CMFC).",

author = "Park, {C. W.} and Lee, {C. H.} and Kim, {J. H.} and M. Park",

year = "2001",

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AU - Lee, C. H.

AU - Kim, J. H.

AU - Park, M.

PY - 2001/9

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N2 - In this paper, in order to control uncertain chaotic system, an adaptive fuzzy control (AFC) scheme is developed for the multi-input/multi-output plants represented by the Takagi-Sugeno (T-S) fuzzy models. The proposed AFC scheme provides robust tracking of a desired signal for the T-S fuzzy systems with uncertain parameters. The developed control law and adaptive law guarantee the boundedness of all signals in the closed-loop system. In addition, the chaotic state tracks the state of the stable reference model (SRM) asymptotically with time for any bounded reference input signal. The suggested AFC design technique is applied to control of a uncertain Lorenz system based on T-S fuzzy model such os stabilization, synchronization and chaotic model following control (CMFC).

AB - In this paper, in order to control uncertain chaotic system, an adaptive fuzzy control (AFC) scheme is developed for the multi-input/multi-output plants represented by the Takagi-Sugeno (T-S) fuzzy models. The proposed AFC scheme provides robust tracking of a desired signal for the T-S fuzzy systems with uncertain parameters. The developed control law and adaptive law guarantee the boundedness of all signals in the closed-loop system. In addition, the chaotic state tracks the state of the stable reference model (SRM) asymptotically with time for any bounded reference input signal. The suggested AFC design technique is applied to control of a uncertain Lorenz system based on T-S fuzzy model such os stabilization, synchronization and chaotic model following control (CMFC).

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