Adaptive Synchronization of Uncertain Chaotic Systems Based on T–S Fuzzy Model

Kim, Jae‐Hun; Hyun, Chang‐Ho; Kim, Euntai; Park, Mignon

doi:10.1109/tfuzz.2006.880007

Cited by 66 publications

(25 citation statements)

References 13 publications

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“…), (Hou, Y.;Zurada, J. M.;Karwowski, W.;Marras, W. S. & Davis, K. (2007)), (XianTu, P. (1990)), (Gang, F. (2006)), (Lian, K.-Y. ;Su, C.-H. & Huang, C.-S. (2006)), (Hseng, T.;Li, S. & Tsai, S.-H. (2007)) and (Kim, J.-H.;Hyun, C.-H.;Kim, E. & Park, M. (2007).).…”

Section: Overview Of Identification and Estimation Of Fuzzy Systemsmentioning

confidence: 99%

Extended Kalman Filter for the Estimation and Fuzzy Optimal Control of Takagi-Sugeno Model

Jiménez¹,

Al‐Hadithi²,

Matı́a³

2011

Fuzzy Controllers, Theory and Applications

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Section: Overview Of Identification and Estimation Of Fuzzy Systemsmentioning

confidence: 99%

Extended Kalman Filter for the Estimation and Fuzzy Optimal Control of Takagi-Sugeno Model

Jiménez¹,

Al‐Hadithi²,

Matı́a³

2011

Fuzzy Controllers, Theory and Applications

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“…Remark 7 Assumption 1 holds in many practical systems, such as track trail systems [2,5], chaotic systems [17], DC motor systems [6] and so on.…”

Section: (Ax) T = Ax (4) (Xa) T = Xamentioning

confidence: 99%

“…During the last two decades, considerable attention has been attracted to describing nonlinear systems using T-S fuzzy models. Thus, many important results have been reported in [2,5,6,11,17,18,20,21,33,34,37], but most of these results are only about stability analysis and state feedback stabilization.…”

Section: Introductionmentioning

confidence: 99%

A New Approach to Observer-Based Fault-Tolerant Controller Design for Takagi-Sugeno Fuzzy Systems with State Delay

Zhang

Jiang

Shi

2009

Circuits Syst Signal Process

110

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This paper deals with the problem of active fault-tolerant control (FTC) for time-delay Takagi-Sugeno (T-S) fuzzy systems based on a fuzzy adaptive fault diagnosis observer (AFDO). A novel fuzzy fast adaptive fault estimation (FAFE) algorithm for T-S fuzzy models is proposed to enhance the performance of fault estimation, and sufficient conditions for the existence of the fault estimator are given in terms of linear matrix inequalities (LMIs). Using the obtained on-line fault estimation information, an observer-based fast active fault-tolerant controller is designed to compensate for the effect of faults by stabilizing the closed-loop system. Simulation results of a track trail system and a nonlinear numerical example are presented to illustrate the effectiveness of the proposed method.

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“…In [1], the design of a feedback controller for Chua's chaotic circuit is analyzed, based on a combination of H ∞ control theory, adaptive fuzzy control and Lyapunov's stability method. In [2,3] TakagiSugeno fuzzy modelling is proposed for chaotic systems. A feedback controller is designed based on Lyapunov's stability analysis and the solution of LMIs.…”

Section: Introductionmentioning

confidence: 99%

Flatness-Based Adaptive Neurofuzzy Control of Chaotic Dynamical Systems

Rigatos

Siano

2016

Intell Ind Syst

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The paper proposes a solution to the problem of control of nonlinear chaotic dynamical systems, which is based on differential flatness theory and on adaptive fuzzy control. An adaptive fuzzy controller is designed for chaotic dynamical systems, under the constraint that the system's model is unknown. The control algorithm aims at satisfying the H ∞ tracking performance criterion, which means that the influence of the modeling errors and the external disturbances on the tracking error is attenuated to an arbitrary desirable level. After transforming the chaotic system's model into a linear form, the resulting control inputs are shown to contain nonlinear elements which depend on the system's parameters. The nonlinear terms which appear in the control inputs are approximated with the use of neurofuzzy networks. It is shown that a suitable learning law can be defined for the aforementioned neuro-fuzzy approximators so as to preserve the closed-loop system stability. With the use of Lyapunov stability analysis it is proven that the proposed adaptive fuzzy control scheme results in H ∞ tracking performance. The efficiency of the adaptive fuzzy control method is checked through simulation experiments, using as case study the Lorenz chaotic oscillator.

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Adaptive Synchronization of Uncertain Chaotic Systems Based on T–S Fuzzy Model

Cited by 66 publications

References 13 publications

Extended Kalman Filter for the Estimation and Fuzzy Optimal Control of Takagi-Sugeno Model

Extended Kalman Filter for the Estimation and Fuzzy Optimal Control of Takagi-Sugeno Model

A New Approach to Observer-Based Fault-Tolerant Controller Design for Takagi-Sugeno Fuzzy Systems with State Delay

Flatness-Based Adaptive Neurofuzzy Control of Chaotic Dynamical Systems

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