IDEAS home Printed from https://ideas.repec.org/a/hin/complx/8520835.html
   My bibliography  Save this article

An Exponential Varying-Parameter Neural Network for Repetitive Tracking of Mobile Manipulators

Author

Listed:
  • Ying Kong
  • Qingqing Tang
  • Jingsheng Lei
  • Ruiyang Zhang

Abstract

A novel exponential varying-parameter neural network (EVPNN) is presented and investigated to solve the inverse redundancy scheme of the mobile manipulators via quadratic programming (QP). To suspend the phenomenon of drifting free joints and guarantee high convergent precision of the end effector, the EVPNN model is applied to trajectory planning of mobile manipulators. Firstly, the repetitive motion scheme for mobile manipulators is formulated into a QP index. Secondly, the QP index is transformed into a time-varying matrix equation. Finally, the proposed EVPNN method is used to solve the QP index via the matrix equation. Theoretical analysis and simulations illustrate that the EVPNN solver has an exponential convergent speed and strong robustness in mobile manipulator applications. Comparative simulation results demonstrate that the EVPNN possesses a superior convergent rate and accuracy than the traditional ZNN solver in repetitive trajectory planning with a mobile manipulator.

Suggested Citation

  • Ying Kong & Qingqing Tang & Jingsheng Lei & Ruiyang Zhang, 2020. "An Exponential Varying-Parameter Neural Network for Repetitive Tracking of Mobile Manipulators," Complexity, Hindawi, vol. 2020, pages 1-12, September.
    • Handle: RePEc:hin:complx:8520835
    DOI: 10.1155/2020/8520835
    as

    Download full text from publisher

    File URL: http://downloads.hindawi.com/journals/8503/2020/8520835.pdf
    Download Restriction: no
    File URL: http://downloads.hindawi.com/journals/8503/2020/8520835.xml
    Download Restriction: no
    File URL: https://libkey.io/10.1155/2020/8520835?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:complx:8520835. 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.