IDEAS home Printed from https://ideas.repec.org/a/taf/tsysxx/v54y2023i16p3013-3026.html
   My bibliography  Save this article

Adaptive stochastic model predictive control via network ensemble learning

Author

Listed:
  • Weiliang Xiong
  • Defeng He
  • Jianbin Mu
  • Xiuli Wang

Abstract

This paper proposes a novel ensemble learning-based adaptive stochastic model predictive control (SMPC) algorithm for constrained linear systems with unknown nonlinear terms and random disturbances. The ensemble network combining a feedforward neural network and a Bayesian network is used to offline learn the nonlinear dynamics and disturbance distribution parameters. Then, the mixed-tube scheme is designed to cope with input constraints and state chance constraints while decreasing computational demands and conservativeness. The reliability of the stochastic tube is guaranteed using the Hoeffding inequality-based verification mechanism, which results in a chance constraint with double probabilities. The feasibility and exponential stability of the SMPC are rigorously proven. A numerical example verifies the merits of the proposed algorithm in terms of the control performance and the feasible domain.

Suggested Citation

  • Weiliang Xiong & Defeng He & Jianbin Mu & Xiuli Wang, 2023. "Adaptive stochastic model predictive control via network ensemble learning," International Journal of Systems Science, Taylor & Francis Journals, vol. 54(16), pages 3013-3026, December.
    • Handle: RePEc:taf:tsysxx:v:54:y:2023:i:16:p:3013-3026
    DOI: 10.1080/00207721.2023.2268234
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/00207721.2023.2268234
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/00207721.2023.2268234?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:54:y:2023:i:16:p:3013-3026. 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.