IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v11y2023i21p4511-d1272393.html
   My bibliography  Save this article

A New Fractional-Order Adaptive Sliding-Mode Approach for Fast Finite-Time Control of Human Knee Joint Orthosis with Unknown Dynamic

Author

Listed:
  • Aydin Azizi

    (School of Engineering, Computing and Mathematics, Oxford Brookes University, Wheatley Campus, Oxford OX33 1HX, UK)

  • Mojtaba Naderi Soorki

    (Department of Electrical Engineering, Sharif University of Technology, Tehran 14588-89694, Iran)

  • Tahmineh Vedadi Moghaddam

    (Department of Electrical Engineering, Amirkabir University of Technology, Tehran 15875-4413, Iran)

  • Ali Soleimanizadeh

    (Department of Electrical Engineering, Islamic Azad University South Tehran Branch, Tehran 15875-4413, Iran)

Abstract

This study delves into the implementation of Fast Finite Time Fractional-Order Adaptive Sliding Mode Control (FFOASMC) for knee joint orthosis (KJO) in the presence of undisclosed dynamics. To achieve this, a novel approach introduces a Fractional-Order Sliding Surface (FOSS). In the context of limited knowledge regarding the dynamics of knee joint arthrosis, Fractional-Order Fast Adaptive Sliding Mode Control (FOFASMC) is devised. Its purpose is to ensure both finite-time stability and prompt convergence of the KJO’s state to the desired trajectory. This controller employs adaptive rules to estimate the enigmatic dynamic parameters of KJO. Through the application of the Lyapunov theorem, the attained finite-time stability of the closed loop is demonstrated. Simulation results effectively showcase the viability of these approaches and offer a comparative analysis against conventional integer-order sliding mode controllers.

Suggested Citation

  • Aydin Azizi & Mojtaba Naderi Soorki & Tahmineh Vedadi Moghaddam & Ali Soleimanizadeh, 2023. "A New Fractional-Order Adaptive Sliding-Mode Approach for Fast Finite-Time Control of Human Knee Joint Orthosis with Unknown Dynamic," Mathematics, MDPI, vol. 11(21), pages 1-16, November.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:21:p:4511-:d:1272393
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/11/21/4511/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/11/21/4511/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Peng Yang & Gaowei Zhang & Jie Wang & Xiaozhou Wang & Lili Zhang & Lingling Chen, 2017. "Command Filter Backstepping Sliding Model Control for Lower-Limb Exoskeleton," Mathematical Problems in Engineering, Hindawi, vol. 2017, pages 1-10, November.
    2. Younes Zahraoui & Fardila M. Zaihidee & Mostefa Kermadi & Saad Mekhilef & Marizan Mubin & Jing Rui Tang & Ezrinda M. Zaihidee, 2023. "Fractional Order Sliding Mode Controller Based on Supervised Machine Learning Techniques for Speed Control of PMSM," Mathematics, MDPI, vol. 11(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. Younes Zahraoui & Fardila M. Zaihidee & Mostefa Kermadi & Saad Mekhilef & Ibrahim Alhamrouni & Mehdi Seyedmahmoudian & Alex Stojcevski, 2023. "Optimal Tuning of Fractional Order Sliding Mode Controller for PMSM Speed Using Neural Network with Reinforcement Learning," Energies, MDPI, vol. 16(11), pages 1-17, May.

    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:jmathe:v:11:y:2023:i:21:p:4511-:d:1272393. 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.