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  Improved consensus conditions for multi-agent systems with uncertain topology: the generalized transition rates case

Wang, X., Wang, H., Li, C., Huang, T., Kurths, J. (2020): Improved consensus conditions for multi-agent systems with uncertain topology: the generalized transition rates case. - IEEE Transactions on Network Science and Engineering, 7, 3, 1158-1169.
https://doi.org/10.1109/TNSE.2019.2911713

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 Creators:
Wang, Xin1, Author
Wang, Hui1, Author
Li, Chuandong1, Author
Huang, Tingwen1, Author
Kurths, Jürgen2, Author              
Affiliations:
1External Organizations, ou_persistent22              
2Potsdam Institute for Climate Impact Research, ou_persistent13              

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 Abstract: This paper is dedicated to addressing the consensus issues for a class of multi-agent systems (MASs) subjected to Markovian switching interaction topology and time-varying input delay. In line with this consideration, we first propose a modified reciprocally convex inequality, named as delay-dependent reciprocally convex inequality to obtain a tighter upper bound about the time-derivative of Lyapunov-Krasovskii functional. As a result, an improved condition with less conservatism stated elegantly in terms of linear matrix inequalities is established such that the MASs with Markovian switching topology and time varying input delay can approach the unified state. Further, the general cases of Markovian switching topology where each transition rate is completely unknown or just its estimate value is known are systematically investigated as well. Moreover, to make our results more meaningful, several comparison results concerning the admissible input delay against other related ones are also presented, which yield that our consensus conditions significantly improve the existing ones advocated thus far. Finally, we verify the validity, the effectiveness, and the practical applicability of our results in the simulation examples.

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 Dates: 2020
 Publication Status: Finally published
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1109/TNSE.2019.2911713
MDB-ID: No data to archive
PIKDOMAIN: RD4 - Complexity Science
Research topic keyword: Complex Networks
Research topic keyword: Nonlinear Dynamics
Research topic keyword: Tipping Elements
Model / method: Machine Learning
Organisational keyword: RD4 - Complexity Science
Working Group: Network- and machine-learning-based prediction of extreme events
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Title: IEEE Transactions on Network Science and Engineering
Source Genre: Journal, SCI, Scopus
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Pages: - Volume / Issue: 7 (3) Sequence Number: - Start / End Page: 1158 - 1169 Identifier: Other: Institute of Electrical and Electronics Engineers (IEEE)
Other: 2327-4697
CoNE: https://publications.pik-potsdam.de/cone/journals/resource/IEEE-transactions-network-sience-engineering
Publisher: Institute of Electrical and Electronics Engineers (IEEE)