25703

Artificial Intelligence in Electric Machine Drives: Advances and Trends

Shen Zhang
School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332 USA
arXiv:2110.05403 [eess.SY], (11 Oct 2021)

@misc{zhang2021artificial,

   title={Artificial Intelligence in Electric Machine Drives: Advances and Trends},

   author={Shen Zhang},

   year={2021},

   eprint={2110.05403},

   archivePrefix={arXiv},

   primaryClass={eess.SY}

}

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This review paper systematically summarizes the existing literature on applying classical AI techniques and advanced deep learning algorithms to electric machine drives. It is anticipated that with the rapid progress in deep learning models and embedded hardware platforms, AI-based data-driven approaches will become increasingly popular for the automated high-performance control of electric machines. Additionally, this paper also provides some outlook towards promoting its widespread application in the industry, such as implementing advanced RL algorithms with good domain adaptation and transfer learning capabilities and deploying them onto low-cost SoC FPGA devices.
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