Fuzzy Optimization Using Extended Kalman Filter

Abstract : Fuzzy Logic is based on the idea that in fuzzy sets each element in the set can assume a value from 0 to 1, not only 0 or 1, as in crisp set theory. The degree of membership function is defined as the gradation in the extent to which an element is belonging to the relevant sets. Optimizing the membership functions of a fuzzy system can be viewed as a system identification problem for nonlinear dynamic system. In this paper two input and one output fuzzy controller is designed for the dynamic process of aircraft. The addition of an EKF in the feedback loop improved the system response by blocking possible effects of measurement error based on PredictorCorrector algorithm. An Extended Kalman Filter approach to optimize the membership functions of the inputs and outputs of the fuzzy controller. The performance of the fuzzy system before and after the optimization are compared, as well as the membership functions.