Invariance preserving control of clusters recognized in networks of kuramoto oscillators

Research outputpeer-review

1 Citation (Scopus)

Abstract

The Kuramoto model is able to describe a huge variety of examples of synchronization in the real world. We re-consider it through the framework of the network science and study the phenomenon of a particular interest, agent clustering. We assume that clusters are already recognized by some algorithm and then consider them as new variables on mesoscopic scale, which allows one to significantly reduce the dimensionality of a complicated (complex) system, thus reducing the required number of control inputs. In contrast to the common approach, where each agent is treated separately, we propose an alternative one using a supplementary control input, which is equal for the whole cluster. We also perform an analysis of this input by finding its limitations required for cluster structure to remain invariant in a network of Kuramoto oscillators. The theoretical results are demonstrated on a simulated multi-agent network with multiple clusters.

Original languageEnglish
Title of host publicationArtificial Intelligence
Subtitle of host publication18th Russian Conference, RCAI 2020, Proceedings
EditorsSergei O. Kuznetsov, Aleksandr I. Panov, Konstantin S. Yakovlev
PublisherSpringer Nature
Pages472-486
Number of pages15
ISBN (Electronic)9783030595357
ISBN (Print)9783030595340
DOIs
Publication statusPublished - 2020
Event18th Russian Conference on Artificial Intelligence, RCAI 2020 - Moscow
Duration: 10 Oct 202016 Oct 2020

Publication series

NameLecture Notes in Computer Science
Volume12412 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference18th Russian Conference on Artificial Intelligence, RCAI 2020
CountryRussian Federation
CityMoscow
Period10/10/2016/10/20

Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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