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

Nana Yang, Junmin Li and Jiaxi Chen

Applied Mathematics and Computation, 2020, vol. 375, issue C

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.

Keywords: Fully distributed; Hybrid adaptive consensus protocols; Non-linearly parameterized multi-agent systems; Iterative learning control Lyapunov–Krasovskii functional (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0096300320300436
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:apmaco:v:375:y:2020:i:c:s0096300320300436

DOI: 10.1016/j.amc.2020.125074

Access Statistics for this article

Applied Mathematics and Computation is currently edited by Theodore Simos

More articles in Applied Mathematics and Computation from Elsevier
Bibliographic data for series maintained by Catherine Liu ().