Fuzzy controller for rapid nickel-cadmium batteries charger through adaptive neuro-fuzzy inference system (ANFIS) architecture

Abstract

ANFIS architecture is a class of adaptive networks, which is functionally equivalent to fuzzy inference systems. The architecture has been employed for fuzzy modeling that learns information about a data-set in order to compute the membership functions and rule-base that best follow the given input-output data. ANFIS employs hybrid learning that combines the gradient method and the least squares estimates to identify premise and consequent parameters respectively. In this paper the fuzzy controller for rapidly charging nickel-cadmium (Ni-Cd) batteries charger has been designed through ANFIS. The behavior of Ni-Cd batteries was not known for higher charging rates and the input-output data of batteries has been obtained through rigorous experimentation with an objective to charge the batteries as quickly as possible, but without doing any damage to them. Takagi-Sugeno-Kang (TSK) model has been considered for the controller.