IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i15p5629-d1203054.html
   My bibliography  Save this article

Virtual Signal Calculation Using Radial Neural Model Applied in a State Controller of a Two-Mass System

Author

Listed:
  • Radoslaw Stanislawski

    (Department of Electrical Machines, Drives and Measurements, Faculty of Electrical Engineering, Wroclaw University of Science and Technology, 50-372 Wroclaw, Poland)

  • Jules-Raymond Tapamo

    (School of Engineering, University of KwaZulu-Natal, Durban 4041, South Africa)

  • Marcin Kaminski

    (Department of Electrical Machines, Drives and Measurements, Faculty of Electrical Engineering, Wroclaw University of Science and Technology, 50-372 Wroclaw, Poland)

Abstract

Neural network approaches have commonly been used to solve complex mathematical equations in the literature. They have inspired the modifications of state controllers and are often implemented for electrical drives with an elastic connection. Given that the addition of a virtual signal can provide adaptive properties to classical controllers and that selected feedback signals can also be replaced with a virtual state variable from a neural network, several combinations can be considered and compared. In this paper, R adial B asis F unction neural-network-based control algorithms are proposed in which online updating of the output weights is used. Analyses of simulation experiment results reveal that the proposed control algorithms significantly improve the operation of classic-state feedback controllers applied to two-mass systems in the presence of parameter uncertainty.

Suggested Citation

  • Radoslaw Stanislawski & Jules-Raymond Tapamo & Marcin Kaminski, 2023. "Virtual Signal Calculation Using Radial Neural Model Applied in a State Controller of a Two-Mass System," Energies, MDPI, vol. 16(15), pages 1-23, July.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:15:p:5629-:d:1203054
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/15/5629/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/16/15/5629/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Radoslaw Stanislawski & Jules-Raymond Tapamo & Marcin Kaminski, 2022. "A Hybrid Adaptive Controller Applied for Oscillating System," Energies, MDPI, vol. 15(17), pages 1-22, August.
    2. Chen, Chuin-Shan & Noorizadegan, Amir & Young, D.L. & Chen, C.S., 2023. "On the selection of a better radial basis function and its shape parameter in interpolation problems," Applied Mathematics and Computation, Elsevier, vol. 442(C).
    3. Rafal Szczepanski & Marcin Kaminski & Tomasz Tarczewski, 2020. "Auto-Tuning Process of State Feedback Speed Controller Applied for Two-Mass System," Energies, MDPI, vol. 13(12), pages 1-16, June.
    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. Marcin Kamiński & Krzysztof Szabat, 2021. "Adaptive Control Structure with Neural Data Processing Applied for Electrical Drive with Elastic Shaft," Energies, MDPI, vol. 14(12), pages 1-26, June.
    2. Mateusz Malarczyk & Mateusz Zychlewicz & Radoslaw Stanislawski & Marcin Kaminski, 2023. "Electric Drive with an Adaptive Controller and Wireless Communication System," Future Internet, MDPI, vol. 15(2), pages 1-20, January.
    3. Krzysztof Szabat & Tomasz Pajchrowski & Tomasz Tarczewski, 2021. "Modern Electrical Drives: Trends, Problems, and Challenges," Energies, MDPI, vol. 15(1), pages 1-4, December.
    4. Mengnan Chen & Yongquan Zhou & Qifang Luo, 2022. "An Improved Arithmetic Optimization Algorithm for Numerical Optimization Problems," Mathematics, MDPI, vol. 10(12), pages 1-27, June.

    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:5629-:d:1203054. 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.