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A novel sensorless control method for SRMs based on gain-optimized SMO

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Abstract

To achieve high precision and robust control of switched reluctance machines (SRMs), this paper proposes a novel rotor position estimation method based on gain-optimized sliding mode observer (SMO) with neural networks and whale optimization algorithm. For the SMO, this paper uses the easily measured phase current to constitute the error function of the SMO, which requires less pre-stored data. Using the neural networks’ powerful nonlinear mapping capability, the relationship between the sliding mode gains and speed estimation error and the position estimation error is obtained, and then the whale optimization algorithm is used to find the optimal sliding mode gains in the restricted range. The simulation and experiment results show that the accuracy of the SMO control system is significantly improved.

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Funding

This work was supported by National Natural Science Foundation of China (52107055, 51877179), China Postdoctoral Science Foundation (2021M691996), Fundamental Research Funds for the Central Universities (3102021ZDHQD03) and the Aeronautical Science Foundation of China (20200040053001).

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Authors and Affiliations

  1. Northwestern Polytechnical University, Xi’an, 710072, China

    Lefei Ge, Dongpeng Zhang, Jiale Huang, Zhaoyang Fu & Shoujun Song

Authors
  1. Lefei Ge
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  2. Dongpeng Zhang
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  3. Jiale Huang
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  4. Zhaoyang Fu
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  5. Shoujun Song
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Contributions

LG and ZF conceived of the presented idea; LG and DZ wrote the paper; DZ and JH developed the theory and performed the computations and experiments; ZF and SS were involved in data duration and supervised the findings of this work; all authors discussed the results and contributed by providing critical feedback and helped shape the research, analysis and manuscript.

Corresponding author

Correspondence to Zhaoyang Fu.

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This work was supported by National Natural Science Foundation of China (52107055, 51877179), Key Research and Development Program of Shaanxi Province (2022GY-260), China Postdoctoral Science Foundation (2021M691996), and Fundamental Research Funds for the Central Universities (3102021ZDHQD03).

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Ge, L., Zhang, D., Huang, J. et al. A novel sensorless control method for SRMs based on gain-optimized SMO. Electr Eng 106, 2011–2019 (2024). https://doi.org/10.1007/s00202-023-02042-8

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  • DOI: https://doi.org/10.1007/s00202-023-02042-8

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