Speed Estimation of Brushless Direct Current (BLDC) Motor with Multilayer Perceptron

Abstract

This study used an artificial neural network model to estimate the revolutions per minute of a brushless direct current (BLDC) motor operating at different driver modes and different load currents. The dataset that was used to train and test the artificial neural network model was obtained from experimental applications and was made applicable for the training of a multilayer perceptron. A total of 7643 data items were used in the study. Of these data, 382 were used to test the ANN model. Test results indicated that the multilayer perceptron provided 99.34% estimation and that the target and the results were quite close.