IDEAS home Printed from https://ideas.repec.org/a/eee/apmaco/v515y2026ics0096300325005740.html

A learning-based value iteration scheme for singularly perturbed systems and its application in RC ladder circuit

Author

Listed:
  • Wang, Zihan
  • Yang, Qing
  • Shen, Hao

Abstract

In this paper, a value iteration (VI) scheme considering the guaranteed state convergence rate (GSCR) is proposed for multi-input singularly perturbed systems (MISPSs) with unknown system dynamics. Unlike traditional reduced-order methods, a full-order modeling approach is adopted for MISPSs, thereby avoiding the suboptimality of reduction techniques. In contrast to classical differential games, this work explicitly considers the state convergence rate under multiple interacting inputs. Specifically, the control policy design is formulated as solving a guaranteed game algebraic Riccati equation (GGARE). To solve GGARE, an online model-free VI algorithm is developed, which obtains the solutions to Nash equilibrium in real-time using measured state and input data. Compared to existing algorithms, the proposed algorithm offers three key advantages: i) No initial stable control gain is needed; ii) Rapid state convergence is achieved; iii) The information on system dynamics is not required. Finally, the effectiveness of the proposed method is validated through an RC ladder circuit example.

Suggested Citation

  • Wang, Zihan & Yang, Qing & Shen, Hao, 2026. "A learning-based value iteration scheme for singularly perturbed systems and its application in RC ladder circuit," Applied Mathematics and Computation, Elsevier, vol. 515(C).
    • Handle: RePEc:eee:apmaco:v:515:y:2026:i:c:s0096300325005740
    DOI: 10.1016/j.amc.2025.129849
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0096300325005740
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.amc.2025.129849?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

    for a different version of it.

    References listed on IDEAS

    as
    1. Huang, He & Xu, Jiawei & Wang, Jing & Chen, Xiangyong, 2024. "Reinforcement learning-based secure synchronization for two-time-scale complex dynamical networks with malicious attacks," Applied Mathematics and Computation, Elsevier, vol. 479(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Li, Yan & Hang, Qirun & Zha, Lijuan & Liu, Jinliang, 2026. "Secure observer-driven synchronization for complex networks constrained by partial information exchange and limited communication bandwidth," Applied Mathematics and Computation, Elsevier, vol. 510(C).

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    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:eee:apmaco:v:515:y:2026:i:c:s0096300325005740. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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.