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Reduced-Order \(H_{\infty }\) Filtering with Intermittent Measurements for a Class of 2D Systems

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Abstract

The problem of \(H_{\infty }\) filter design for a class of 2D systems is solved here in the presence of intermittent measurements. Data dropouts are characterized using a stochastic variable satisfying the Bernoulli random binary distribution. Our attention is focused on the design of reduced-order \(H_{\infty }\) filters such that the filtering error 2D stochastic system is robust mean-square asymptotically stable and fulfills a given \(H_{\infty }\) disturbance attenuation level. We use a new formulation for a class of 2D system Fornasini–Marchesini (FM) models. A sufficient condition is established by means of the linear matrix inequalities (LMI) technique. The efficiency and viability of the proposed techniques and tools are demonstrated through a set of numerical examples.

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Correspondence to Bensalem Boukili.

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Boukili, B., Hmamed, A. & Tadeo, F. Reduced-Order \(H_{\infty }\) Filtering with Intermittent Measurements for a Class of 2D Systems. J Control Autom Electr Syst 27, 597–607 (2016). https://doi.org/10.1007/s40313-016-0271-1

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