IDEAS home Printed from https://ideas.repec.org/a/taf/tsysxx/v55y2024i7p1363-1376.html
   My bibliography  Save this article

Moving horizon estimation based on distributionally robust optimisation

Author

Listed:
  • Aolei Yang
  • Hao Wang
  • Qing Sun
  • Minrui Fei

Abstract

This paper presents a novel moving horizon estimation approach based on distributionally robust optimisation to tackle the state estimation problem of non-linear systems with missing noise distribution information. The proposed method adopts a fuzzy set to mitigate the impact of uncertainties on state estimation. Specifically, the method derives an empirical distribution within the prediction window using a priori data and constructs a fuzzy sphere set using the Wasserstein metric with the empirical distribution as the sphere centre. This enables the estimation of the state sequence under the worst probability distribution of the fuzzy set. To demonstrate the effectiveness of the proposed method, a simple simulation example is conducted to compare its performance with that of traditional moving horizon estimation. The results provide evidence of the feasibility and superiority of the proposed approach.

Suggested Citation

  • Aolei Yang & Hao Wang & Qing Sun & Minrui Fei, 2024. "Moving horizon estimation based on distributionally robust optimisation," International Journal of Systems Science, Taylor & Francis Journals, vol. 55(7), pages 1363-1376, May.
    • Handle: RePEc:taf:tsysxx:v:55:y:2024:i:7:p:1363-1376
    DOI: 10.1080/00207721.2024.2305691
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/00207721.2024.2305691
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/00207721.2024.2305691?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:55:y:2024:i:7:p:1363-1376. 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.