Pulse injection-based sensorless switched reluctance motor driver model with machine learning algorithms

Abstract

In this study relationships of pulse injected idle phase currents are used to predict rotor position with tuned fine tree and ensemble bagged tree algorithm in MATLAB. Different classifier algorithms trained, tested, and the best accurate results are obtained via ensemble bagged tree classifier using idle phase currents. Three-phase 6/4 switched reluctance motor (SRM) with optical position sensors diagnosis pulses has been injected into idle phases and operated at constant load and speed. The measured idle phase currents were rearranged using the time series method and trained with supervised machine learning algorithms. These unprocessed idle phase currents reduce processing time and contribute to the real-time operation of the system. This study proves that SRM can be driven by predicting the active phase to be triggered by trained ensemble bagged tree and tuned fine tree machine learning algorithms from real-time measured idle phase current data.