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

Abstract

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.

References in corpus (10)

• Continuous control with deep reinforcement learning
• FINN: A Framework for Fast, Scalable Binarized Neural Network Inference
• Machine Learning and Deep Learning Algorithms for Bearing Fault Diagnostics -- A Comprehensive Review
• A Neural-Network-Based Model Predictive Control of Three-Phase Inverter With an Output LC Filter
• Data-Driven Permanent Magnet Temperature Estimation in Synchronous Motors with Supervised Machine Learning
• Thermal Neural Networks: Lumped-Parameter Thermal Modeling With State-Space Machine Learning
• ZynqNet: An FPGA-Accelerated Embedded Convolutional Neural Network
• Deep Learning-based Prediction of Key Performance Indicators for Electrical Machine
• The implementation of a Deep Recurrent Neural Network Language Model on a Xilinx FPGA
• An On-Device Federated Learning Approach for Cooperative Model Update between Edge Devices

Cited by in corpus (2)

• Review of Machine Learning Techniques for Power Electronics Control and Optimization
• Reinforcement learning for hardware acceleration and system-level optimization on FPGA and integrated-circuit platforms: A survey