IDEAS home Printed from https://ideas.repec.org/a/wly/complx/v2022y2022i1n4936167.html

Design and Implementation of a Novel Intelligent Strategy for the Permanent Magnet Synchronous Motor Emulation

Author

Listed:
  • Hamed Zeinoddini-Meymand
  • Salah Kamel
  • Baseem Khan

Abstract

In this paper, an intelligent neural network‐based controller is designed and implemented to control the speed of a permanent magnet synchronous motor (PMSM). First, the exact mathematical model of PMSM is presented, and then, by designing a controller, we apply the wind turbine emulation challenges. The designed controller for the first time is implemented on a Arm Cortex‐M microcontroller and tested on a laboratory PMSM. Since online learning neural network on a chip requires a strong processor, high memory, and convergence guarantee, this article uses the offline method. In this method, first, for different work points, the neural network is trained by local controllers, and then, the trained network is implemented on the chip and used. Uncertainty in the parameters and the effect of load torque as challenges of control systems are applied in the proposed method, and a comparison with other methods is performed in the implementation results section.

Suggested Citation

  • Hamed Zeinoddini-Meymand & Salah Kamel & Baseem Khan, 2022. "Design and Implementation of a Novel Intelligent Strategy for the Permanent Magnet Synchronous Motor Emulation," Complexity, John Wiley & Sons, vol. 2022(1).
    • Handle: RePEc:wly:complx:v:2022:y:2022:i:1:n:4936167
    DOI: 10.1155/2022/4936167
    as

    Download full text from publisher

    File URL: https://doi.org/10.1155/2022/4936167
    Download Restriction: no
    File URL: https://libkey.io/10.1155/2022/4936167?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Jafar Tavoosi & Amir Abolfazl Suratgar & Mohammad Bagher Menhaj & Amir Mosavi & Ardashir Mohammadzadeh & Ehsan Ranjbar, 2021. "Modeling Renewable Energy Systems by a Self-Evolving Nonlinear Consequent Part Recurrent Type-2 Fuzzy System for Power Prediction," Sustainability, MDPI, vol. 13(6), pages 1-21, 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. Panyu Tang & Mahdi Aghaabbasi & Mujahid Ali & Amin Jan & Abdeliazim Mustafa Mohamed & Abdullah Mohamed, 2022. "How Sustainable Is People’s Travel to Reach Public Transit Stations to Go to Work? A Machine Learning Approach to Reveal Complex Relationships," Sustainability, MDPI, vol. 14(7), pages 1-18, March.
    2. Fatemehsadat Mirshafiee & Emad Shahbazi & Mohadeseh Safi & Rituraj Rituraj, 2023. "Predicting Power and Hydrogen Generation of a Renewable Energy Converter Utilizing Data-Driven Methods: A Sustainable Smart Grid Case Study," Energies, MDPI, vol. 16(1), pages 1-20, January.
    3. Hasnat Bin Tariq & Naveed Ishtiaq Chaudhary & Zeshan Aslam Khan & Muhammad Asif Zahoor Raja & Khalid Mehmood Cheema & Ahmad H. Milyani, 2021. "Maximum-Likelihood-Based Adaptive and Intelligent Computing for Nonlinear System Identification," Mathematics, MDPI, vol. 9(24), pages 1-23, December.

    More about this item

    Statistics

    Access and download statistics

    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:wly:complx:v:2022:y:2022:i:1:n:4936167. 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: Wiley Content Delivery (email available below). General contact details of provider: https://onlinelibrary.wiley.com/journal/8503 .

    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.