A rotor position estimator for switched reluctance motors using CMAC

Abstract

This paper presents an approach to the rotor position estimation in switched reluctance motors (SRMs) by using a cerebellum model articulation controller (CMAC). Previous research has shown that an artificial neural network (ANN) forms an efficient mapping structure for the nonlinear SRM. Through measurement of the flux linkages and currents for the phases, a feedforward neural network (FFNN) is able to estimate the rotor position. CMAC is investigated in this paper in order to overcome high computational power requirement problem which is encountered in feedforward ANN based rotor position estimator. The issues involved in designing, training and implementing CMAC are presented. In order to demonstrate the feasibility of the concept, a 20 kW, 6/4, 3-phase SRM is studied with training and evaluation data, which are obtained from a simulation program. A CMAC which is based on experimentally measured training and testing data for the same SRM is also used to demonstrate the promise of this approach.