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Observer-based distributed adaptive iterative learning control for linear multi-agent systems

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  • Jinsha Li
  • Sanyang Liu
  • Junmin Li

Abstract

This paper investigates the consensus problem for linear multi-agent systems from the viewpoint of two-dimensional systems when the state information of each agent is not available. Observer-based fully distributed adaptive iterative learning protocol is designed in this paper. A local observer is designed for each agent and it is shown that without using any global information about the communication graph, all agents achieve consensus perfectly for all undirected connected communication graph when the number of iterations tends to infinity. The Lyapunov-like energy function is employed to facilitate the learning protocol design and property analysis. Finally, simulation example is given to illustrate the theoretical analysis.

Suggested Citation

  • Jinsha Li & Sanyang Liu & Junmin Li, 2017. "Observer-based distributed adaptive iterative learning control for linear multi-agent systems," International Journal of Systems Science, Taylor & Francis Journals, vol. 48(14), pages 2948-2955, October.
    • Handle: RePEc:taf:tsysxx:v:48:y:2017:i:14:p:2948-2955
    DOI: 10.1080/00207721.2017.1365966
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    Cited by:

    1. Liming Wang & Guoshan Zhang, 2019. "Performance Index Based Observer-Type Iterative Learning Control for Consensus Tracking of Uncertain Nonlinear Fractional-Order Multiagent Systems," Complexity, Hindawi, vol. 2019, pages 1-17, November.

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