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A fuzzy filter with missing measurement for observer-based T-S fuzzy models

This paper is concerned with the problem of a fuzzy filter of nonlinear system with missing measurements. The nonlinear system is represented by a Takagi-Sugeno(TS) fuzzy model. The system measurements may be unavailable at any sample time and the probability of the occurrence of missing data is assumed to be known. The purpose of this problem is to design a linear filter such that, the error state of the filtering process is mean square bounded. A basis-dependent Lyapunov function approach is developed to design the fuzzy filter, and it is developed the upper bound of a fuzzy filter gain of the estimation error subject to some LMI constraints. In this situation, the estimation error due to persistent bounded disturbances. Finally, an illustrative numerical example is provided to show the effectiveness of the proposed approach.