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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