L2-L∞ Filtering for Takagi–Sugeno fuzzy neural networks based on Wirtinger-type inequalities

Choi, Hyun Duck; Ahn, Choon Ki; Shi, Peng; Lim, Myo Taeg; Song, Moon Kyou

doi:10.1016/j.neucom.2014.11.046

Cited by 43 publications

(15 citation statements)

References 45 publications

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“…Remark So far, many of the researchers have investigated T‐S fuzzy systems through various approaches, for instance see . Very recently, the authors in have addressed the problem of sampled‐data H ∞ control of uncertain active suspension systems based on fuzzy model approach.…”

Section: Resultsmentioning

confidence: 99%

“…More precisely, the key feature of T‐S fuzzy model is to express a nonlinear system by the time‐varying convex combination of linear state space models using nonlinear fuzzy membership functions belonging to the unit simplex in a compact set of the state variables . Subsequently, a great number of stability analysis and control synthesis results for the class of T‐S fuzzy systems in both continuous‐time and discrete‐time contexts have been extensively discussed in the literature, for example, see and references therein. To mention a few, in , the positive filtering problem has been examined for positive T‐S fuzzy systems using l 1 ‐induced performance constraint, where sufficient conditions are expressed in the form of linear programming problems to obtain the desired filters and in , by constructing nonquadratic membership‐dependent Lyapunov function, some new less conservative stability conditions have been developed for T‐S fuzzy systems.…”

Section: Introductionmentioning

confidence: 99%

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Sampled‐data reliable stabilization of T‐S fuzzy systems and its application

Sakthivel

Kaviarasan

et al. 2016

Complexity

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In this article, based on sampled‐data approach, a new robust state feedback reliable controller design for a class of Takagi–Sugeno fuzzy systems is presented. Different from the existing fault models for reliable controller, a novel generalized actuator fault model is proposed. In particular, the implemented fault model consists of both linear and nonlinear components. Consequently, by employing input‐delay approach, the sampled‐data system is equivalently transformed into a continuous‐time system with a variable time delay. The main objective is to design a suitable reliable sampled‐data state feedback controller guaranteeing the asymptotic stability of the resulting closed‐loop fuzzy system. For this purpose, using Lyapunov stability theory together with Wirtinger‐based double integral inequality, some new delay‐dependent stabilization conditions in terms of linear matrix inequalities are established to determine the underlying system's stability and to achieve the desired control performance. Finally, to show the advantages and effectiveness of the developed control method, numerical simulations are carried out on two practical models. © 2016 Wiley Periodicals, Inc. Complexity 21: 518–529, 2016

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Sampled‐data reliable stabilization of T‐S fuzzy systems and its application

Sakthivel

Kaviarasan

et al. 2016

Complexity

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“…At the same time, the T-S fuzzy neural network method is employed to predict the propylene conversion. A T-S fuzzy system is often used as a typical research object (Choi et al, 2015; He et al, 2017; Wang et al, 2017). As an intelligent soft sensor method, the neural network and fuzzy techniques are combined together (Eseye et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Soft sensor modelling of propylene conversion based on a Takagi-Sugeno fuzzy neural network optimized with independent component analysis and mutual information

Liu

Gao

et al. 2018

Transactions of the Institute of Measurement and Control

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Propylene conversion is important to economic efficiency in the production of acrylic acid. Hence, the online measurement of propylene conversion is becoming more and more important. The current measurement method is mainly uses an offline meteorological chromatography analyser, which is difficult to measure accurately in real time. A soft sensor modelling method of propylene conversion based on Takagi-Sugeno (T-S) fuzzy neural network optimized by independent component analysis and mutual information is proposed in this paper. Firstly, fast independent component analysis-based denoising strategy is developed to remove the noise in the measurement of variables influenced by propylene conversion. Then, a mutual information-based variable selection method is proposed to select the key variables from multitudinous variables to reduce the influence of weak correlation. Finally, a T-S fuzzy neural network algorithm is employed to forecast the propylene conversion in the process of propylene oxidation. Simulation results show that the proposed soft sensor modelling method has better prediction accuracy and generalization ability. The method of this paper is obvious and effective.

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“…The main objective of this theory is to minimise the worst possible impact of persistent bounded disturbances on the system. See for example [31–33, 37] and the references therein. Specifically, in [32], based on T–S fuzzy model, a novel static output feedback fuzzy controller has been developed to minimise the

l_{\infty}

gain of non‐linear discrete‐time systems with persistent bounded disturbances.…”

Section: Introductionmentioning

confidence: 99%

Synchronisation and anti‐synchronisation of chaotic systems with application to DC–DC boost converter

Sakthivel

Santra

Anthoni

et al. 2017

IET Generation, Transmission & Distribution

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L2-L∞ Filtering for Takagi–Sugeno fuzzy neural networks based on Wirtinger-type inequalities

Cited by 43 publications

References 45 publications

Sampled‐data reliable stabilization of T‐S fuzzy systems and its application

Sampled‐data reliable stabilization of T‐S fuzzy systems and its application

Soft sensor modelling of propylene conversion based on a Takagi-Sugeno fuzzy neural network optimized with independent component analysis and mutual information

Synchronisation and anti‐synchronisation of chaotic systems with application to DC–DC boost converter

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