Abstract
Fuzzy logic controllers are applied to various industrial and non-linear systems, however, their control rules and membership functions are usually obtained by time-consuming trial and error procedure. This paper presents a hybrid method for determining the fuzzy rules and membership functions simultaneously. The optimization process consists of a genetic algorithm (GA) which determines the rule base, and an extended Kalman filter (EKF) approach for tuning the parameters of membership functions. The procedure discussed in this study is illustrated on a simple automotive cruise control problem. By comparing nominal and optimized fuzzy controllers, we demonstrate that the hybrid algorithm, as a combination of genetic algorithm and extended Kalman filter, can be an effective tool for improving the performance of a fuzzy controller. In other words, the fuzzy controller thus designed can implement simpler in the real world applications, by using a few fuzzy variables.
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