IDEAS home Printed from https://ideas.repec.org/a/eee/apmaco/v493y2025ics0096300324007173.html
   My bibliography  Save this article

Reduced-order full information estimation observer-based dissipative control for nonlinear discrete-time switched singular systems with unknown inputs

Author

Listed:
  • Shi, Hongpeng
  • Zhao, Zhenli
  • Ma, Shuping

Abstract

The dissipative control problem for nonlinear discrete-time switched singular systems (NDSSSs) is investigated via a novel reduced-order observer in this paper. Firstly, based on the generalized Sylvester equations and the introduced nonlinear injection term, a novel reduced-order observer is designed for each subsystem. The designed reduced-order observer can still produce accurate full-information estimation even though the dynamics and the output of the systems both contain unknown inputs. Then, by using average dwell-time scheme and multi-Lyapunov functions, some new sufficient conditions are proposed such that the resulted closed-loop NDSSSs are regular and causal, have a unique solution, and are globally uniformly asymptotically stable with a strict (Q,S,V)-γ-dissipative. A novel relaxation technique is proposed for decoupling nonlinear inequalities involving products of multiple nonsquare unknown variables. The design procedures of reduced-order observer and controller are presented by a specific algorithm. Finally, two numerical examples and an electronic circuit example are provided to illustrate the effectiveness of the theoretical results.

Suggested Citation

  • Shi, Hongpeng & Zhao, Zhenli & Ma, Shuping, 2025. "Reduced-order full information estimation observer-based dissipative control for nonlinear discrete-time switched singular systems with unknown inputs," Applied Mathematics and Computation, Elsevier, vol. 493(C).
  • Handle: RePEc:eee:apmaco:v:493:y:2025:i:c:s0096300324007173
    DOI: 10.1016/j.amc.2024.129256
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0096300324007173
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.amc.2024.129256?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:apmaco:v:493:y:2025:i:c:s0096300324007173. 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: https://www.journals.elsevier.com/applied-mathematics-and-computation .

    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.