IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i15p5694-d1206246.html
   My bibliography  Save this article

Multi-Adjustment Strategy for Phase Current Reconstruction of Permanent Magnet Synchronous Motors Based on Model Predictive Control

Author

Listed:
  • Zhiming Liao

    (National Maglev Transportation Engineering R&D Center, Tongji University, Shanghai 200092, China)

  • Tianran Peng

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

  • Jia Liu

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

  • Tao Guo

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

Abstract

In response to the model predictive control (MPC) driving system, this paper proposes a multi-adjustment strategy for phase current reconstruction based on a coupled current sampling method. The proposed coupled current sampling method eliminates the need to modify the inverter’s internal wiring. The current signals utilized in the proposed method are all external current signals from the inverter and do not involve any current signals from the internal circuitry of the inverter. By analyzing the current sampling mechanism of duty-cycle model predictive control (DC-MPC) as a modulation method, the underlying principles of the non-reconstructible current regions in the coupled current sampling method are revealed. The non-reconstructible regions are accurately delineated into low and high-modulation regions using coupled current sampling. A multi-adjustment strategy for phase current reconstruction is proposed to address the non-reconstructible regions. In the low-modulation regions, phase current reconstruction is achieved through compensated voltage vector pulse injection. In the high-modulation regions, phase current reconstruction is accomplished using the zero-voltage vector insertion approximation method, which maintains the symmetry of the PWM waveform and avoids current distortion. Experimental results on a permanent magnet synchronous motor validate the effectiveness and feasibility of the proposed approach.

Suggested Citation

  • Zhiming Liao & Tianran Peng & Jia Liu & Tao Guo, 2023. "Multi-Adjustment Strategy for Phase Current Reconstruction of Permanent Magnet Synchronous Motors Based on Model Predictive Control," Energies, MDPI, vol. 16(15), pages 1-16, July.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:15:p:5694-:d:1206246
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/15/5694/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/16/15/5694/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Nan Jin & Chao Pan & Yanyan Li & Shiyang Hu & Jie Fang, 2020. "Model Predictive Control for Virtual Synchronous Generator with Improved Vector Selection and Reconstructed Current," Energies, MDPI, vol. 13(20), pages 1-16, October.
    2. Hao Yu & Jiajun Wang & Zhuangzhuang Xin, 2022. "Model Predictive Control for PMSM Based on Discrete Space Vector Modulation with RLS Parameter Identification," Energies, MDPI, vol. 15(11), pages 1-16, May.
    3. Tianjiao Luan & Zhichao Wang & Yang Long & Zhen Zhang & Qi Li & Zhihao Zhu & Chunhua Liu, 2021. "Multi-Virtual-Vector Model Predictive Current Control for Dual Three-Phase PMSM," Energies, MDPI, vol. 14(21), pages 1-17, November.
    4. Ibrahim Farouk Bouguenna & Ahmed Tahour & Ralph Kennel & Mohamed Abdelrahem, 2021. "Multiple-Vector Model Predictive Control with Fuzzy Logic for PMSM Electric Drive Systems," Energies, MDPI, vol. 14(6), pages 1-23, March.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Paolo Mercorelli, 2022. "Model Predictive Control for Energy Optimization in Generators/Motors as Well as Converters and Inverters for Futuristic Integrated Power Networks," Energies, MDPI, vol. 15(16), pages 1-4, August.
    2. João Inácio Da Silva Filho & Raphael Adamelk Bispo de Oliveira & Marcos Carneiro Rodrigues & Hyghor Miranda Côrtes & Alexandre Rocco & Mauricio Conceição Mario & Dorotéa Vilanova Garcia & Jair Minoro , 2023. "Predictive Controller Based on Paraconsistent Annotated Logic for Synchronous Generator Excitation Control," Energies, MDPI, vol. 16(4), pages 1-25, February.
    3. Jaime A. Rohten & Javier E. Muñoz & Esteban S. Pulido & José J. Silva & Felipe A. Villarroel & José R. Espinoza, 2021. "Very Low Sampling Frequency Model Predictive Control for Power Converters in the Medium and High-Power Range Applications," Energies, MDPI, vol. 14(1), pages 1-18, January.
    4. Muhammad Usama & Jaehong Kim, 2021. "Low-Speed Transient and Steady-State Performance Analysis of IPMSM for Nonlinear Speed Regulator with Effective Compensation Scheme," Energies, MDPI, vol. 14(20), pages 1-16, October.
    5. Silvio Simani & Elena Zattoni, 2021. "Advanced Control Design and Fault Diagnosis," Energies, MDPI, vol. 14(18), pages 1-6, September.
    6. Yalin Liang & Yuyao He & Yun Niu, 2022. "Robust Errorless-Control-Targeted Technique Based on MPC for Microgrid with Uncertain Electric Vehicle Energy Storage Systems," Energies, MDPI, vol. 15(4), pages 1-23, February.
    7. Mingmao Hu & Feng Yang & Yi Liu & Liang Wu, 2022. "Finite Control Set Model-Free Predictive Current Control of a Permanent Magnet Synchronous Motor," Energies, MDPI, vol. 15(3), pages 1-18, January.
    8. Thyago Estrabis & Gabriel Gentil & Raymundo Cordero, 2021. "Development of a Resolver-to-Digital Converter Based on Second-Order Difference Generalized Predictive Control," Energies, MDPI, vol. 14(2), pages 1-22, January.
    9. Khoudir Kakouche & Adel Oubelaid & Smail Mezani & Djamila Rekioua & Toufik Rekioua, 2023. "Different Control Techniques of Permanent Magnet Synchronous Motor with Fuzzy Logic for Electric Vehicles: Analysis, Modelling, and Comparison," Energies, MDPI, vol. 16(7), pages 1-28, March.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:16:y:2023:i:15:p:5694-:d:1206246. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.