Fuzzy Model based Suboptimal Kalman Filter with Missing Measurement

This paper is concerned with the optimal fuzzy filtering of a nonlinear observer system with a missing measurements. The nonlinear observer system is represented by a Takagi-Sugeno(TS) fuzzy model. The system measurements may be unavailable, and the occurrence of missing data is assumed to be known. The purpose of this optimal filtering problem is to design a suboptimal estimator with a missing measurement, and this method is guaranteed by the convergence of the residual suboptimal filtering process.