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Research on Model Predictive Control of IPMSM Based on Adaline Neural Network Parameter Identification

Author

Listed:
  • Lihui Wang

    (School of Electrical and Power Engineering, China University of Mining and Technology, Xuzhou 221116, China)

  • Guojun Tan

    (School of Electrical and Power Engineering, China University of Mining and Technology, Xuzhou 221116, China)

  • Jie Meng

    (School of Electrical and Power Engineering, China University of Mining and Technology, Xuzhou 221116, China)

Abstract

This paper reports the optimal control problem on the interior permanent magnet synchronous motor (IPMSM) systems. The control performance of the traditional model predictive control (MPC) controller is ruined due to the parameter uncertainty and mismatching. In order to solve the problem that the MPC algorithm has a large dependence on system parameters, a method which integrates MPC control method and parameter identification for IPMSM is proposed. In this method, the d-q axis inductances and rotor permanent magnet flux of IPMSM motor are identified by the Adaline neural network algorithm, and then, the identification results are applied to the predictive controller and maximum torque per ampere (MTPA) module. The experimental results show that the optimized MPC control proposed in this paper has a good steady state and robust performance.

Suggested Citation

  • Lihui Wang & Guojun Tan & Jie Meng, 2019. "Research on Model Predictive Control of IPMSM Based on Adaline Neural Network Parameter Identification," Energies, MDPI, vol. 12(24), pages 1-16, December.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:24:p:4803-:d:298695
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    Citations

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    Cited by:

    1. Karol Wróbel & Piotr Serkies & Krzysztof Szabat, 2020. "Model Predictive Base Direct Speed Control of Induction Motor Drive—Continuous and Finite Set Approaches," Energies, MDPI, vol. 13(5), pages 1-15, March.
    2. Zehao Lyu & Xiang Wu & Jie Gao & Guojun Tan, 2021. "An Improved Finite-Control-Set Model Predictive Current Control for IPMSM under Model Parameter Mismatches," Energies, MDPI, vol. 14(19), pages 1-13, October.

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