An approach for sensorless position estimation for switched reluctance motors using artifical neural networks

This paper presents a new approach to the sensorless control of the switched-reluctance motor (SRM). The basic premise of the method is that an artificial neural network (ANN) forms a very efficient mapping structure for the nonlinear SRM. Through measurement of the phase flux linkages and phase currents the neural network is able to estimate the rotor position, thereby facilitating elimination of the rotor position sensor. The ANN training data set is comprised of magnetization data for the SRM with flux linkage (lambda) and current (i) as inputs and the corresponding position (theta) as output in this set. Given a sufficiently large training data set, the ANN can build up a correlation among lambda, i and theta for an appropriate network architecture. This paper presents the development, implementation, and operation of an ANN-based position estimator for a three-phase SRM.