Title: Improved L₂ - L_∞ T-S fuzzy filtering for discrete stochastic systems

Authors: Taha Zoulagh; Bensalem Boukili; Abdelaziz Hmamed; Ahmed El Hajjaji

Addresses: LESSI, Department of Physics, Faculty of Sciences Dhar El Mehraz, B.P. 1796 Fes-Atlas, Morocco ' LESSI, Department of Physics, Faculty of Sciences Dhar El Mehraz, B.P. 1796 Fes-Atlas, Morocco ' LESSI, Department of Physics, Faculty of Sciences Dhar El Mehraz, B.P. 1796 Fes-Atlas, Morocco ' UFR of Sciences, MIS, University of Picardie Jules Verne, Amiens, France

Abstract: The problem of L₂ - L_∞ filtering for a class of Takagi-Sugeno (T-S) fuzzy stochastic systems is tackled in this paper. A new design method of full order filter is presented, that guarantees the asymptotic stability of filtering error system with a prescribed L₂ - L_∞ performance level. The filter parameters are assumed to be linearly dependent on the normalised fuzzy weighting functions. Sufficient analysis condition is proposed in the filter analysis step where some known lemmas are applied, thereafter, the corresponding filter design is cast into a convex optimisation problem. Finally, numerical example from literature is employed to demonstrate the effectiveness of the proposed method.

Keywords: L₂ - L_∞ filtering; Takagi-Sugeno fuzzy systems; stochastic systems; slack variables; linear matrix inequalities; LMIs.

DOI: 10.1504/IJDSSS.2018.093195

International Journal of Digital Signals and Smart Systems, 2018 Vol.2 No.2, pp.150 - 166

Received: 28 Jun 2017
Accepted: 06 Feb 2018
Published online: 13 Jul 2018 *

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