AI-driven, Model-Free Current Control: A Deep Symbolic Approach for Optimal Induction Machine Performance
<|reference_start|>AI-driven, Model-Free Current Control: A Deep Symbolic Approach for Optimal Induction Machine Performance: This paper proposed a straightforward and efficient current control solution for induction machines employing deep symbolic regression (DSR). The proposed DSR-based control design offers a simple yet highly effective approach by creating an optimal control model through training and fitting, resulting in an analytical dynamic numerical expression that characterizes the data. Notably, this approach not only produces an understandable model but also demonstrates the capacity to extrapolate and estimate data points outside its training dataset, showcasing its adaptability and resilience. In contrast to conventional state-of-the-art proportional-integral (PI) current controllers, which heavily rely on specific system models, the proposed DSR-based approach stands out for its model independence. Simulation and experimental tests validate its effectiveness, highlighting its superior extrapolation capabilities compared to conventional methods. These findings pave the way for the integration of deep learning methods in power conversion applications, promising improved performance and adaptability in the control of induction machines. The simulation and experimental test results are provided with a 3.7 kw induction machine to verify the efficacy of the proposed control solution.<|reference_end|>
@article{usama2024ai-driven,,
    title={AI-driven, Model-Free Current Control: A Deep Symbolic Approach for
  Optimal Induction Machine Performance},
    author={Muhammad Usama, Yunkyung Hwang, and Jaehong Kim},
    journal={arXiv preprint arXiv:2405.08277},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.08277},
    primaryClass={eess.SY cs.SY}
}