IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v11y2023i23p4841-d1292519.html
   My bibliography  Save this article

Adaptive Event-Triggered Neural Network Fast Finite-Time Control for Uncertain Robotic Systems

Author

Listed:
  • Jianhui Wang

    (School of Mechanical and Electrical Engineering, Guangzhou University, Guangzhou 510006, China)

  • Yongping Du

    (School of Mechanical and Electrical Engineering, Guangzhou University, Guangzhou 510006, China)

  • Yuanqing Zhang

    (School of Mechanical and Electrical Engineering, Guangzhou University, Guangzhou 510006, China)

  • Yixiang Gu

    (School of Mechanical and Electrical Engineering, Guangzhou University, Guangzhou 510006, China)

  • Kairui Chen

    (School of Mechanical and Electrical Engineering, Guangzhou University, Guangzhou 510006, China
    School of Computer & Information, Qiannan Normal University for Nationalities, Duyun 558000, China)

Abstract

A fast convergence adaptive neural network event-triggered control strategy is proposed for the trajectory tracking issue of uncertain robotic systems with output constraints. To cope with the constraints on the system output in the actual industrial field while reducing the burden on communication resources, an adaptive event-triggered mechanism is designed by using logarithm-type barrier Lyapunov functions and an event-triggered mechanism. Meanwhile, the combination of neural networks and fast finite-time stability theory can not only approximate the unknown nonlinear function of the system, but also construct the control law and adaptive law with a fractional exponential power to accelerate the system’s convergence speed. Furthermore, the tracking errors converge quickly to a bounded and adjustable compact set in finite time. Finally, the effectiveness of the strategy is verified by simulation examples.

Suggested Citation

  • Jianhui Wang & Yongping Du & Yuanqing Zhang & Yixiang Gu & Kairui Chen, 2023. "Adaptive Event-Triggered Neural Network Fast Finite-Time Control for Uncertain Robotic Systems," Mathematics, MDPI, vol. 11(23), pages 1-15, December.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:23:p:4841-:d:1292519
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/11/23/4841/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/11/23/4841/
    Download Restriction: no
    ---><---

    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:23:p:4841-:d:1292519. 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.

    We have no bibliographic references for this item. You can help adding them by using 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.