IDEAS home Printed from https://ideas.repec.org/a/taf/tsysxx/v47y2016i13p3116-3131.html
   My bibliography  Save this article

Bipartite output consensus in networked multi-agent systems of high-order power integrators with signed digraph and input noises

Author

Listed:
  • Hongwen Ma
  • Derong Liu
  • Ding Wang
  • Biao Luo

Abstract

In this paper, we concentrate on investigating bipartite output consensus in networked multi-agent systems of high-order power integrators. Systems with power integrator are ubiquitous among weakly coupled, unstable and underactuated mechanical systems. In the presence of input noises, an adaptive disturbance compensator and a technique of adding power integrator are introduced to the complex nonlinear multi-agent systems to reduce the deterioration of system performance. Additionally, due to the existence of negative communication weights among agents, whether bipartite output consensus of high-order power integrators can be achieved remains unknown. Therefore, it is of great importance to study this issue. The underlying idea of designing the distributed controller is to combine the output information of each agent itself and its neighbours, the state feedback within its internal system and input adaptive noise compensator all together. When the signed digraph is structurally balanced, bipartite output consensus can be reached. Finally, numerical simulations are provided to verify the validity of the developed criteria.

Suggested Citation

  • Hongwen Ma & Derong Liu & Ding Wang & Biao Luo, 2016. "Bipartite output consensus in networked multi-agent systems of high-order power integrators with signed digraph and input noises," International Journal of Systems Science, Taylor & Francis Journals, vol. 47(13), pages 3116-3131, October.
    • Handle: RePEc:taf:tsysxx:v:47:y:2016:i:13:p:3116-3131
    DOI: 10.1080/00207721.2015.1090039
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/00207721.2015.1090039
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/00207721.2015.1090039?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. Fenglan Sun & Zhi-Hong Guan & Li Ding & Yan-Wu Wang, 2013. "Mean square average-consensus for multi-agent systems with measurement noise and time delay," International Journal of Systems Science, Taylor & Francis Journals, vol. 44(6), pages 995-1005.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Peng, Zhinan & Hu, Jiangping & Shi, Kaibo & Luo, Rui & Huang, Rui & Ghosh, Bijoy Kumar & Huang, Jiuke, 2020. "A novel optimal bipartite consensus control scheme for unknown multi-agent systems via model-free reinforcement learning," Applied Mathematics and Computation, Elsevier, vol. 369(C).

    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. Sabir Djaidja & Qinghe Wu, 2015. "Leader-following consensus for single-integrator multi-agent systems with multiplicative noises in directed topologies," International Journal of Systems Science, Taylor & Francis Journals, vol. 46(15), pages 2788-2798, November.
    2. Lei Liu & Jinjun Shan, 2017. "robust synchronisation of nonlinear multi-agent systems with sampled-data information," International Journal of Systems Science, Taylor & Francis Journals, vol. 48(1), pages 138-149, January.
    3. Yilun Shang, 2015. "Group consensus of multi-agent systems in directed networks with noises and time delays," International Journal of Systems Science, Taylor & Francis Journals, vol. 46(14), pages 2481-2492, October.
    4. Bin Hu & Zhi-Hong Guan & Rui-Quan Liao & Ding-Xue Zhang & Gui-Lin Zheng, 2015. "Consensus-based distributed optimisation of multi-agent networks via a two level subgradient-proximal algorithm," International Journal of Systems Science, Taylor & Francis Journals, vol. 46(7), pages 1307-1318, May.
    5. Sun, Fenglan & Wang, Rui & Zhu, Wei & Li, Yongfu, 2019. "Flocking in nonlinear multi-agent systems with time-varying delay via event-triggered control," Applied Mathematics and Computation, Elsevier, vol. 350(C), pages 66-77.

    More about this item

    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:taf:tsysxx:v:47:y:2016:i:13:p:3116-3131. 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/TSYS20 .

    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.