Machine Learning for the Control and Monitoring of Electric Machine Drives: Advances and Trends

Abstract

This review article systematically summarizes the existing literature on utilizing machine learning (ML) techniques for the control and monitoring of electric machine drives. It is anticipated that with the rapid progress in learning algorithms and specialized embedded hardware platforms, ML-based data-driven approaches will become standard tools for the automated high-performance control and monitoring of electric drives. In addition, this article also provides some outlook toward promoting its widespread application in the industry with a focus on deploying ML algorithms onto embedded system-on-chip field-programmable gate array devices.