mlsanityData to 7 Oct 2026

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

arXiv 2110.05403 ↗First posted 11 Oct 2021eess.SY cs.AI cs.SYcited by 1

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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.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 18 of its 257 references that are in the map. First posted on arXiv 11 Oct 2021.

Builds on

18 of 257 references.

References · 18

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6 more
  1. 2021Thermal Neural Networks: Lumped-Parameter Thermal Modeling With State-Space Machine Learningcited by 3
  2. 2020Optimal Tracking Current Control of Switched Reluctance Motor Drives Using Reinforcement Q-learning Schedulingcited by 2
  3. 2017The implementation of a Deep Recurrent Neural Network Language Model on a Xilinx FPGAcited by 1
  4. 2019Visualization of Multi-Objective Switched Reluctance Machine Optimization at Multiple Operating Conditions with t-SNEcited by 1
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  6. 2020Data Set Description: Identifying the Physics Behind an Electric Motor -- Data-Driven Learning of the Electrical Behavior (Part II)cited by 1

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Citing papers per year: 2025: 1.

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  1. 2025VeriPy -- A New Python-Based Approach for SDR Pipelined/Unrolled Hardware Accelerator Generationcited by 0