IDEAS home Printed from https://ideas.repec.org/a/taf/tsysxx/v51y2020i15p3030-3043.html
   My bibliography  Save this article

Optimisation of control and learning actions for a repetitive-control system based on Takagi–Sugeno fuzzy model

Author

Listed:
  • Manli Zhang
  • Min Wu
  • Luefeng Chen
  • Shengnan Tian
  • Jinhua She

Abstract

This paper deals with the problem of designing a two-dimensional (2D) modified repetitive-control system based on a Takagi–Sugeno (T-S) fuzzy model to achieve high tracking performance for a nonlinear plant. First, a nonlinear plant is represented by a T-S fuzzy model, and a modified repetitive controller with two repetitive loops is used to increases design flexibility. Next, a continuous-discrete 2D model is established to make use of the 2D characteristics in the modified repetitive-control system. Then, a sufficient stability condition is derived in terms of linear matrix inequalities. Three parameters are used to balance continuous control and discrete learning actions: one in a repetitive loop and two in a Lyapunov–Krasovskii functional. A particle swarm optimisation algorithm yields optimal parameters and the gains of the modified repetitive and state-feedback controllers. Finally, simulation and comparison results demonstrate the effectiveness of our method.

Suggested Citation

  • Manli Zhang & Min Wu & Luefeng Chen & Shengnan Tian & Jinhua She, 2020. "Optimisation of control and learning actions for a repetitive-control system based on Takagi–Sugeno fuzzy model," International Journal of Systems Science, Taylor & Francis Journals, vol. 51(15), pages 3030-3043, November.
    • Handle: RePEc:taf:tsysxx:v:51:y:2020:i:15:p:3030-3043
    DOI: 10.1080/00207721.2020.1807651
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/00207721.2020.1807651
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/00207721.2020.1807651?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    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:taf:tsysxx:v:51:y:2020:i:15:p:3030-3043. 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/TSYS20 .

    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.