$H_{\bm \infty}$ Fuzzy Filtering of Nonlinear Systems With Intermittent Measurements

This paper is concerned with the problem of
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fuzzy filtering of nonlinear systems with intermittent measurements. The nonlinear plant is represented by a Takagi-Sugeno (T-S) fuzzy model. The measurements transmission from the plant to the filter is assumed to be imperfect, and a stochastic variable satisfying the Bernoulli random binary distribution is utilized to model the phenomenon of the missing measurements. Attention is focused on the design of an
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_infin
filter such that the filter error system is stochastically stable and preserves a guaranteed
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_infin
performance. A basis-dependent Lyapunov function approach is developed to design the
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filter. By introducing some slack matrix variables, the coupling between the Lyapunov matrix and the system matrices is eliminated, which greatly facilitates the filter-design procedure. The developed theoretical results are in the form of linear matrix inequalities (LMIs). Finally, an illustrative example is provided to show the effectiveness of the proposed approach.