Sampled-data H∞ fuzzy filtering for nonlinear systems with missing measurements

In this paper, a sampled-data H fuzzy filtering problem is considered for nonlinear systems with missing measurements. The nonlinear sampled-data system and missing measurements are assumed to be represented by a TakagiSugeno (TS) fuzzy system and an independent, identically distributed Bernoulli random process, respectively. Based on the fuzzy system, the H fuzzy filtering problem is formulated to design the sampled-data fuzzy filter. By using the exponential mean-square stability definition, the stability condition with an H performance is guaranteed for the fuzzy system with the sampled-data fuzzy filter, and its sufficient condition is converted into the linear matrix inequality (LMI) format. Finally, an example is provided to verify the effectiveness of the proposed fuzzy filtering technique.

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