Scientific and Technical Journal


ISSN Print 2221-3937
ISSN Online 2221-3805

Nowadayscreation of sensorless control systems is still an actual task. The elimination of the mechanical rotor position sensor increases reliability and reduces costs. The switched reluctance motor (SRM) is a highly nonlinear machine, that makes it an ideal candidate for the application of artificial neural networks (ANNs). In the worksof various authors the neural networks use the measurement of the phase flux linkages and phase currents as inputs to estimate the corresponding rotor position.

This paper presents a new sensorless control system of switched reluctance motorswith a more simple structure of the neural network than existing ones.  A feedforward artificial neural network, that uses only phase currents and supply voltage as the input signals, with the mathematical processing of rotor position data as the network output was synthesized and researched on the experimental plant.

The results of experiment are presented and they show the effectiveness of proposed neuro-estimator. 


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