Robust H∞ fuzzy filter design for uncertain nonlinear singularly perturbed systems with Markovian jumps: An LMI approach

Assawinchaichote, Wudhichai; Nguang, Sing Kiong; Shi, Peng; Agarwal, Ramesh K.

doi:10.1016/j.ins.2006.10.006

Cited by 97 publications

(49 citation statements)

References 30 publications

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“…Different from the concept of robustness in MJSs control [19,20] and filtering [15,[21][22][23], RFDF design problems should consider increasing the robustness of the residual to modeling errors and unknown inputs as well as enhancing the sensitivity to faults. In this point, the H ∞ norm from unknown input to the residual is widely accepted to estimate the contribution of unknown input to residual [24][25][26].…”

Section: Remarkmentioning

confidence: 99%

Fuzzy model‐based fault detection for Markov jump systems

Liu

2008

Intl J Robust & Nonlinear

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SUMMARYThe robust fault detection filter (RFDF) design problems are studied for nonlinear stochastic time-delay Markov jump systems. By means of the Takagi-Sugeno fuzzy models, the fuzzy RFDF system and the dynamics of filtering error generator are constructed. Moreover, taking into account the sensitivity to faults while guaranteeing robustness against unknown inputs, the H ∞ filtering scheme is proposed to minimize the influences of the unknown inputs and another new performance index is introduced to enhance the sensitivity to faults. A sufficient condition is first established on the stochastic stability using stochastic Lyapunov-Krasovskii function. Then in terms of linear matrix inequalities techniques, the sufficient conditions on the existence of fuzzy RFDF are presented and proved. Finally, the design problem is formulated as a two-objective optimization algorithm. Simulation results illustrate that the proposed RFDF can detect the faults shortly after the occurrences.

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Section: Remarkmentioning

confidence: 99%

Fuzzy model‐based fault detection for Markov jump systems

Liu

2008

Intl J Robust & Nonlinear

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“…Therefore, the study for delayed T-S fuzzy systems has not only theoretical importance but also practical significance. Among these solutions, time delay and uncertain phenomena [10][11][12][13][14] are also widely developed.…”

Section: Introductionmentioning

confidence: 99%

Non-fragile finite-time filter design for time-delayed Markovian jumping systems via T–S fuzzy model approach

2015

Nonlinear Dyn

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In this paper, the non-fragile finite-time filtering problem is studied for a class of nonlinear Markovian jumping systems (NMJSs) with time delays and uncertainties. To design the mode-dependent nonfragile state filter, Takagi-Sugeno (T-S) fuzzy models are employed to represent the time-delayed and uncertain NMJSs. Then, based on the Lyapunov-Krasovskii functional, a sufficient condition is derived for the existence of a desired non-fragile H ∞ filter which also guarantees the finite-time boundness of the filtering error dynamical NMJSs. By this criterion, the approach to designing a non-fragile fuzzy filter is developed in terms of linear matrix inequalities. Finally, a numerical simulation is provided to illustrate the performance of the proposed method.

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“…In this framework, the problem of T-S fuzzy control and filtering has long been a fascinating focus of research attracting constant attention. It is not surprising that there has been a rich body of relevant literature published in the past two decades; see, for example, [10][11][12][13][14][15][16][17][18][19] for control problems and [20][21][22][23][24][25][26] for filtering issues.…”

Section: Introductionmentioning

confidence: 99%

Analysis, Filtering, and Control for Takagi-Sugeno Fuzzy Models in Networked Systems

Zhang

Wang

et al. 2015

Abstract and Applied Analysis

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The fuzzy logic theory has been proven to be effective in dealing with various nonlinear systems and has a great success in industry applications. Among different kinds of models for fuzzy systems, the so-called Takagi-Sugeno (T-S) fuzzy model has been quite popular due to its convenient and simple dynamic structure as well as its capability of approximating any smooth nonlinear function to any specified accuracy within any compact set. In terms of such a model, the performance analysis and the design of controllers and filters play important roles in the research of fuzzy systems. In this paper, we aim to survey some recent advances on the T-S fuzzy control and filtering problems with various network-induced phenomena. The network-induced phenomena under consideration mainly include communication delays, packet dropouts, signal quantization, and randomly occurring uncertainties (ROUs). With such network-induced phenomena, the developments on T-S fuzzy control and filtering issues are reviewed in detail. In addition, some latest results on this topic are highlighted. In the end, conclusions are drawn and some possible future research directions are pointed out.

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Robust H∞ fuzzy filter design for uncertain nonlinear singularly perturbed systems with Markovian jumps: An LMI approach

Cited by 97 publications

References 30 publications

Fuzzy model‐based fault detection for Markov jump systems

Fuzzy model‐based fault detection for Markov jump systems

Non-fragile finite-time filter design for time-delayed Markovian jumping systems via T–S fuzzy model approach

Analysis, Filtering, and Control for Takagi-Sugeno Fuzzy Models in Networked Systems

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