IDEAS home Printed from https://ideas.repec.org/a/eee/chsofr/v174y2023ics0960077923007968.html
   My bibliography  Save this article

Iterative learning control for continuous-time multi-agent differential inclusion systems with full learnability

Author

Listed:
  • Zhou, Min
  • Wang, JinRong
  • Shen, Dong

Abstract

The learnability of systems plays a crucial role in the feasibility of control objective. This study explores the learnability and iterative learning-based consensus control for continuous-time multi-agent differential inclusion systems. Unlike discrete-time systems, analysis for the relationship between output space and realizable output space that used to explore learnability is more tough since involving continuous-time system output. Using measure theory, the systems exhibit full learnability if and only if the input–output coupling matrix is of full-row rank. Considering the possibility of controller power dissipation and demand for improving tracking performance, iterative learning control with state feedback and an efficiency factor is proposed. The consensus performance for the proposed control scheme is strictly explored. Simulation on unmanned vehicles in freeway demonstrates the validity of the theoretical results.

Suggested Citation

  • Zhou, Min & Wang, JinRong & Shen, Dong, 2023. "Iterative learning control for continuous-time multi-agent differential inclusion systems with full learnability," Chaos, Solitons & Fractals, Elsevier, vol. 174(C).
    • Handle: RePEc:eee:chsofr:v:174:y:2023:i:c:s0960077923007968
    DOI: 10.1016/j.chaos.2023.113895
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960077923007968
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.chaos.2023.113895?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.

    References listed on IDEAS

    as
    1. Nana Yang & Suoping Li & Xianming Zhang, 2022. "Distributed Coordination for a Class of High-Order Multiagent Systems Subject to Actuator Saturations by Iterative Learning Control," Complexity, Hindawi, vol. 2022, pages 1-18, February.
    2. Jian Liu & Yamiao Zhang & Xiaoe Ruan, 2019. "Iterative learning control for a class of uncertain nonlinear systems with current state feedback," International Journal of Systems Science, Taylor & Francis Journals, vol. 50(10), pages 1889-1901, July.
    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. Luo, Hongwei & Wang, JinRong & Shen, Dong, 2023. "Learning ability analysis for linear discrete delay systems with iteration-varying trial length," Chaos, Solitons & Fractals, Elsevier, vol. 171(C).
    2. Yan Geng & Shouqin Wang & Xiaoe Ruan, 2022. "The Convergence of Data-Driven Optimal Iterative Learning Control for Linear Multi-Phase Batch Processes," Mathematics, MDPI, vol. 10(13), pages 1-19, July.

    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:chsofr:v:174:y:2023:i:c:s0960077923007968. 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: Thayer, Thomas R. (email available below). General contact details of provider: https://www.journals.elsevier.com/chaos-solitons-and-fractals .

    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.