IDEAS home Printed from https://ideas.repec.org/a/taf/tsysxx/v53y2022i4p870-882.html
   My bibliography  Save this article

Learning ability of iterative learning control system with a randomly varying trial length

Author

Listed:
  • Yamiao Zhang
  • Jian Liu
  • Xiaoe Ruan

Abstract

This paper investigates the learning ability of iterative learning control (ILC) system with a randomly varying trial length (RVTL). The randomness of trial length is modelled as a discrete stochastic sequence. Firstly, we study the output controllability of the control system over a finite time interval. It is shown that the control system is completely controllable if and only if the input–output coupling matrix (IOCM) is full-row rank. Secondly, we propose an intermittent learning scheme with state feedback. It is strictly proved that under appropriate constraints on the learning gain and the IOCM, the ILC system with RVTL has full learning ability if and only if the probability that the trial length is equal to the desired one is greater than zero. There must exist state feedback such that the ILC process is monotonically convergent. Meanwhile, we illustrate that the proposed convergence conditions can ensure the almost sure convergence property of system output. Finally, an example is given to illustrate the merits of the ILC system with state feedback.

Suggested Citation

  • Yamiao Zhang & Jian Liu & Xiaoe Ruan, 2022. "Learning ability of iterative learning control system with a randomly varying trial length," International Journal of Systems Science, Taylor & Francis Journals, vol. 53(4), pages 870-882, March.
    • Handle: RePEc:taf:tsysxx:v:53:y:2022:i:4:p:870-882
    DOI: 10.1080/00207721.2021.1976306
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/00207721.2021.1976306
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/00207721.2021.1976306?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.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Luo, Hongwei & Wang, JinRong & Shen, Dong, 2023. "Learning ability analysis for linear discrete delay systems with iteration-varying trial length," Chaos, Solitons & Fractals, Elsevier, vol. 171(C).

    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:53:y:2022:i:4:p:870-882. 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.