Guaranteed-cost fuzzy filter design for a class of nonlinear discrete-time uncertain systems

Abstract

In general, it is a difficult work to design an efficient filter for nonlinear systems. This paper studies fuzzy filtering design for nonlinear discrete-time systems. First, the Takagi and Sugeno fuzzy model is proposed to approximate a nonlinear discrete-time system. Next, based on the fuzzy model, the fuzzy estimation for nonlinear discrete-time systems is studied. Using a suboptimal approach, the minimum variance fuzzy estimation problems are characterized in terms of an eigenvalue problem (EVP) by minimizing the upper bound on the variance of the estimation error. The EVP can be solved very efficiently using convex optimization techniques.