Intelligent identification methods for rotor resistance parameter of induction motor drive

Abstract

This paper presents two intelligent identification approaches for rotor resistance of an indirect vector controlled induction motor drive. First approach is based on fuzzy logic control (FLC) able to compensate for variations and errors, FLC scheme was employed to overcome the lack of a precise mathematical model of the process. In the second approach is based on artificial neural networks (ANNs), the error between the rotor flux linkages based on a neural network model and a voltage model is back propagated to adjust the weights of the neural network model for the rotor resistance estimation. The performances of the two intelligent approaches are investigated and compared in simulation. © Springer-Verlag Berlin Heidelberg 2013.