IDEAS home Printed from https://ideas.repec.org/a/hin/jnlmpe/6490167.html
   My bibliography  Save this article

Adaptive Predefined Performance Neural Control for Robotic Manipulators with Unknown Dead Zone

Author

Listed:
  • Shifen Shao
  • Kaisheng Zhang
  • Jun Li
  • Jirong Wang

Abstract

This paper proposes an adaptive predefined performance neural control scheme for robotic manipulators in the presence of nonlinear dead zone. A neural network (NN) is utilized to estimate the model uncertainties and unknown dynamics. An improved funnel function is designed to guarantee the transient behavior of the tracking error. The proposed funnel function can release the assumption on the conventional funnel control. Then, an adaptive predefined performance neural controller is proposed for robotic manipulators, while the tracking errors fall within a prescribed funnel boundary. The closed-loop system stability is proved via Lyapunov function. Finally, the numerical simulation results based on a 2-DOF robotic manipulator illustrate the control effect of the presented approach.

Suggested Citation

  • Shifen Shao & Kaisheng Zhang & Jun Li & Jirong Wang, 2020. "Adaptive Predefined Performance Neural Control for Robotic Manipulators with Unknown Dead Zone," Mathematical Problems in Engineering, Hindawi, vol. 2020, pages 1-8, May.
    • Handle: RePEc:hin:jnlmpe:6490167
    DOI: 10.1155/2020/6490167
    as

    Download full text from publisher

    File URL: http://downloads.hindawi.com/journals/MPE/2020/6490167.pdf
    Download Restriction: no
    File URL: http://downloads.hindawi.com/journals/MPE/2020/6490167.xml
    Download Restriction: no
    File URL: https://libkey.io/10.1155/2020/6490167?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
    ---><---

    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:hin:jnlmpe:6490167. 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: Mohamed Abdelhakeem (email available below). General contact details of provider: https://www.hindawi.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.