Modelling of a nonlinear switched reluctance drive based on artificial neural networks

Switched reluctance motors (SRMs) are increasingly popular machines in electric drives, whose performances are directly related to their operating condition. Their dynamic characteristics vary as conditions change. Recently, several methods of modelling of the magnetic saturation of SRMs have been proposed. However, the SRM is nonlinear and cannot be adequately described by such models. Artificial neural networks (ANNs) may be used to overcome this problem. This paper presents a method which uses a backpropagation algorithm to handle one of the modelling problems in a switched reluctance motor. The simulated waveforms of phase current are compared with those obtained from a commercial switched reluctance motor. Experimental results validate the applicability of the proposed method.<>