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Fully distributed hybrid adaptive learning consensus protocols for a class of non-linearly parameterized multi-agent systems

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  • Yang, Nana
  • Li, Junmin
  • Chen, Jiaxi

Abstract

The fully distributed hybrid adaptive learning consensus problem for a class of non-linearly parameterized multi-agent systems is investigated in this paper. Under the alignment initial condition and by parameter separation technique, Barbalat-like lemma and a novel Lyapunov–Krasovskii functional, the hybrid adaptive learning consensus protocols with time-varying adaptive control gains and differential-difference learning updating laws are presented, which are fully distributed, and the perfect consensus tracking is guaranteed over a finite time interval. Finally, two simulation examples are given to verify the availability and practicability of theoretical results.

Suggested Citation

  • Yang, Nana & Li, Junmin & Chen, Jiaxi, 2020. "Fully distributed hybrid adaptive learning consensus protocols for a class of non-linearly parameterized multi-agent systems," Applied Mathematics and Computation, Elsevier, vol. 375(C).
    • Handle: RePEc:eee:apmaco:v:375:y:2020:i:c:s0096300320300436
    DOI: 10.1016/j.amc.2020.125074
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    References listed on IDEAS

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    5. Jiaxi Chen & Junmin Li, 2018. "Fuzzy adaptive iterative learning coordination control of second-order multi-agent systems with imprecise communication topology structure," International Journal of Systems Science, Taylor & Francis Journals, vol. 49(3), pages 546-556, February.
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