IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v10y2022i2p212-d722007.html
   My bibliography  Save this article

Recursive Identification for MIMO Fractional-Order Hammerstein Model Based on AIAGS

Author

Listed:
  • Qibing Jin

    (Institute of Automation, Beijing University of Chemical Technology, Beijing 100020, China)

  • Bin Wang

    (Institute of Automation, Beijing University of Chemical Technology, Beijing 100020, China)

  • Zeyu Wang

    (Institute of Automation, Beijing University of Chemical Technology, Beijing 100020, China)

Abstract

In this paper, adaptive immune algorithm based on a global search strategy (AIAGS) and auxiliary model recursive least square method (AMRLS) are used to identify the multiple-input multiple-output fractional-order Hammerstein model. The model’s nonlinear parameters, linear parameters, and fractional order are unknown. The identification step is to use AIAGS to find the initial values of model coefficients and order at first, then bring the initial values into AMRLS to identify the coefficients and order of the model in turn. The expression of the linear block is the transfer function of the differential equation. By changing the stimulation function of the original algorithm, adopting the global search strategy before the local search strategy in the mutation operation, and adopting the parallel mechanism, AIAGS further strengthens the original algorithm’s optimization ability. The experimental results show that the proposed method is effective.

Suggested Citation

  • Qibing Jin & Bin Wang & Zeyu Wang, 2022. "Recursive Identification for MIMO Fractional-Order Hammerstein Model Based on AIAGS," Mathematics, MDPI, vol. 10(2), pages 1-21, January.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:2:p:212-:d:722007
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/10/2/212/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/10/2/212/
    Download Restriction: no
    ---><---

    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:gam:jmathe:v:10:y:2022:i:2:p:212-:d:722007. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.