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fuzzy filter for non-linear sampled-data systems under imperfect premise matching

fuzzy filter for non-linear sampled-data systems under imperfect premise matching

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This study proposes an fuzzy filtering technique for non-linear sampled-data systems that are represented on the basis of the Takagi–Sugeno fuzzy model. To improve the performance of the fuzzy filter, an imperfect premise matching condition is considered. An error system between the non-linear system and the fuzzy filter is constructed. In addition, sufficient conditions for showing asymptotic stability and guaranteeing disturbance attenuation performance are proposed in a Lyapunov sense and derived in terms of linear matrix inequalities. Finally, the feasibility of the proposed technique is demonstrated using two simulation examples.

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