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

arXiv: https://arxiv.org/abs/2110.05403 · HTML: https://mlsanity.com/paper/2110.05403/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 257 references that are in the map.
- First posted on arXiv 11 Oct 2021.

## Builds on (most cited first, 18 of 18)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [FINN: A Framework for Fast, Scalable Binarized Neural Network Inference](https://mlsanity.com/paper/1612.07119/) (2016)
- [FPGA/DNN Co-Design: An Efficient Design Methodology for IoT Intelligence on the Edge](https://mlsanity.com/paper/1904.04421/) (2019)
- [A Neural Network-Based On-device Learning Anomaly Detector for Edge Devices](https://mlsanity.com/paper/1907.10147/) (2019)
- [Towards a Reinforcement Learning Environment Toolbox for Intelligent Electric Motor Control](https://mlsanity.com/paper/1910.09434/) (2019)
- [ZynqNet: An FPGA-Accelerated Embedded Convolutional Neural Network](https://mlsanity.com/paper/2005.06892/) (2020)
- [Deep Learning-based Prediction of Key Performance Indicators for Electrical Machine](https://mlsanity.com/paper/2012.11299/) (2020)
- [Data-Driven Permanent Magnet Temperature Estimation in Synchronous Motors with Supervised Machine Learning](https://mlsanity.com/paper/2001.06246/) (2020)
- [A Neural-Network-Based Model Predictive Control of Three-Phase Inverter With an Output LC Filter](https://mlsanity.com/paper/1902.09964/) (2019)
- [An On-Device Federated Learning Approach for Cooperative Model Update between Edge Devices](https://mlsanity.com/paper/2002.12301/) (2020)
- [An FPGA-Based On-Device Reinforcement Learning Approach using Online Sequential Learning](https://mlsanity.com/paper/2005.04646/) (2020)
- [Thermal Neural Networks: Lumped-Parameter Thermal Modeling With State-Space Machine Learning](https://mlsanity.com/paper/2103.16323/) (2021)
- [Optimal Tracking Current Control of Switched Reluctance Motor Drives Using Reinforcement Q-learning Scheduling](https://mlsanity.com/paper/2006.07764/) (2020)
- [The implementation of a Deep Recurrent Neural Network Language Model on a Xilinx FPGA](https://mlsanity.com/paper/1710.10296/) (2017)
- [Visualization of Multi-Objective Switched Reluctance Machine Optimization at Multiple Operating Conditions with t-SNE](https://mlsanity.com/paper/1911.01024/) (2019)
- [Data-Driven Recursive Least Squares Estimation for Model Predictive Current Control of Permanent Magnet Synchronous Motors](https://mlsanity.com/paper/1911.12065/) (2019)
- [Data Set Description: Identifying the Physics Behind an Electric Motor -- Data-Driven Learning of the Electrical Behavior (Part II)](https://mlsanity.com/paper/2003.06268/) (2020)

## Built on by (most cited first, 1 of 1)

- [VeriPy -- A New Python-Based Approach for SDR Pipelined/Unrolled Hardware Accelerator Generation](https://mlsanity.com/paper/2512.00006/) (2025)

## Abstract (arXiv)

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.