Identification of fuzzy controller for rapid Nickel-Cadmium batteries charger through fuzzy c-means clustering algorithm

Abstract

This paper presents the identification of fuzzy controller for rapid Nickel-Cadmium (Ni-Cd) batteries charger by applying fuzzy c-means (FCM) clustering algorithm on the input-output training data. The identification of fuzzy model using input-output data consists of two parts: structure identification and parameter estimation. Structure identification involves the determination of antecedent and consequent variables and in parameter estimation step, antecedents' membership functions and rule consequents are determined. Fuzzy clustering is used to partition the training data into regions that leads to creation of local linear models expressed by fuzzy rules. The data for the batteries charger has been obtained through experimentation with an objective to charge the batteries as fast as possible. For the premise part identification, the input space is partitioned by FCM clustering and the consequent parameters for each rule are calculated as least-square estimate. The Takagi-Sugeno-Kang (TSK) model obtained through FCM clustering algorithm is further fine tuned through hybrid learning.