IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v381y2025ics0306261924024772.html
   My bibliography  Save this article

A new set of multivariable predictive control algorithms for time-delayed nonsquare systems of different domains: A minimum-energy examination

Author

Listed:
  • Hunek, Wojciech P.
  • Feliks, Tomasz

Abstract

A new approach to the minimum-energy design of stochastic inverse model control-oriented predictive control algorithms dedicated to the multivariable physical systems is proposed in the paper. For this reason, the novel transfer-function-type stochastic solutions in the forms of respective continuous-time minimum variance control (CMVC) and discrete-time minimum variance control (DMVC), both employing generalized inverses, are examined. The theoretical and practical simulation examples confirm high advantages of the original σ and Smith factorization-oriented inverses over the benefits derived from the well-established Moore–Penrose inverse regarding the energy-based robustification of the discussed control procedures. Henceforth, from now on, the industrial real-life systems can be developed toward a minimum-energy consumption at the same time preserving the maximum-speed and maximum-accuracy important maintenance for modern sustainable energy plants.

Suggested Citation

  • Hunek, Wojciech P. & Feliks, Tomasz, 2025. "A new set of multivariable predictive control algorithms for time-delayed nonsquare systems of different domains: A minimum-energy examination," Applied Energy, Elsevier, vol. 381(C).
    • Handle: RePEc:eee:appene:v:381:y:2025:i:c:s0306261924024772
    DOI: 10.1016/j.apenergy.2024.125093
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261924024772
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2024.125093?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.

    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:eee:appene:v:381:y:2025:i:c:s0306261924024772. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    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.