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Consensus Tracking of Fractional-Order Multiagent Systems via Fractional-Order Iterative Learning Control

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  • Shuaishuai Lv
  • Mian Pan
  • Xungen Li
  • Qi Ma
  • Tianyi Lan
  • Bingqiang Li
  • Wenyu Cai

Abstract

In this work, the consensus problem of fractional-order multiagent systems with the general linear model of fixed topology is studied. Both distributed - type and - type fractional-order iterative learning control (FOILC) algorithms are proposed. Here, a virtual leader is introduced to generate the desired trajectory, fixed communication topology is considered, and only a subset of followers can access the desired trajectory. The convergence conditions are proved using graph theory, fractional calculus, and λ norm theory. The theoretical analysis shows that the output of each agent completely tracks the expected trajectory in a limited time as the iteration number increases for both - type and - type FOILC algorithms. Extensive numerical simulations are given to demonstrate the feasibility and effectiveness.

Suggested Citation

  • Shuaishuai Lv & Mian Pan & Xungen Li & Qi Ma & Tianyi Lan & Bingqiang Li & Wenyu Cai, 2019. "Consensus Tracking of Fractional-Order Multiagent Systems via Fractional-Order Iterative Learning Control," Complexity, Hindawi, vol. 2019, pages 1-11, November.
    • Handle: RePEc:hin:complx:2192168
    DOI: 10.1155/2019/2192168
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    References listed on IDEAS

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    1. Jing Wang & Tianyu Yang & Gennady Staskevich & Brian Abbe, 2017. "Approximately adaptive neural cooperative control for nonlinear multiagent systems with performance guarantee," International Journal of Systems Science, Taylor & Francis Journals, vol. 48(5), pages 909-920, April.
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