Machine Learning for the Control and Monitoring of Electric Machine Drives: Advances and Trends
It addresses the problem of improving control and monitoring in electric drives for industrial applications, but it is incremental as it reviews existing work.
This review paper summarizes existing literature on using machine learning for electric machine drive control and monitoring, anticipating that advances in algorithms and hardware will make these approaches standard tools for high-performance automation.
This review paper 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, machine learning-based data-driven approaches will become standard tools for the automated high-performance control and monitoring of electric drives. Additionally, this paper also provides some outlook toward promoting its widespread application in the industry with a focus on deploying ML algorithms onto embedded system-on-chip (SoC) field-programmable gate array (FPGA) devices.