Stator and rotor resistance observers for induction motor drive using fuzzy logic and artificial neural networks

This paper presents a new method of on-line estimation for the stator and rotor resistances of the induction motor in the indirect vector controlled drive, using fuzzy logic and artificial neural networks. The back propagation algorithm is used for the training of the neural networks for rotor resistance identification. The error between the desired state variable of an induction motor and the actual state variable of a neural model is back propagated to adjust the weights of the neural model, so that the actual state variable tracks the desired value. A fuzzy logic real time estimator is used as the stator resistance observer, to eliminate the error in rotor resistance estimation. The performance of the induction motor drive with the above rotor and stator resistance estimators, is investigated for torque and flux responses, to analyze the effects of stator resistance observer on rotor resistance identification, for variations in the stator and rotor resistances from their nominal values. Both these resistances are estimated experimentally, in a vector controlled induction motor drive and found to give accurate estimates. The rotor resistance estimation was found to be insensitive to the stator resistance variations both in simulation and experiment.