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  Twisting-based finite-time consensus for Euler-Lagrange systems with an event-triggered strategy

Jin, X., Wei, D., He, W., Kocarev, L., Tang, Y., Kurths, J. (2020): Twisting-based finite-time consensus for Euler-Lagrange systems with an event-triggered strategy. - IEEE Transactions on Network Science and Engineering, 7, 3, 1007-1018.
https://doi.org/10.1109/TNSE.2019.2900264

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Jin, Xin1, Author
Wei, Du1, Author
He, Wangli1, Author
Kocarev, Ljupco1, Author
Tang, Yang1, Author
Kurths, Jürgen2, Author              
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1External Organizations, ou_persistent22              
2Potsdam Institute for Climate Impact Research, ou_persistent13              

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 Abstract: In this paper, a twisting-based consensus algorithm is put forward to deal with the event-triggered finite-time consensus for networked Lagrangian systems with directed graphs. First, a fully distributed event-triggered finite-time protocol is considered, for which we can show that each agent can achieve the consensus after a settling time. In order to remove the requirement of continuous monitoring, a pull-based triggering mechanism is employed. Simultaneously, the Zeno-behavior can be excluded under a finite-time dynamic condition. Then, due to the advantages of non-chattering behaviors and finite-time convergence, a twisting-based consensus algorithm based on homogeneous techniques is developed to drive the Euler-Lagrange systems to the consensus value in a settling time. By means of Pólya's theorem and Sum of Squares tools, a polynomial Lyapunov function is constructed to verify our criteria. At last, we give a numerical example for 2-DOF prototype manipulators to verify the validity of the theoretical results.

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 Dates: 2020-02-19
 Publication Status: Finally published
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1109/TNSE.2019.2900264
MDB-ID: No data to archive
PIKDOMAIN: RD4 - Complexity Science
Research topic keyword: Complex Networks
Research topic keyword: Nonlinear Dynamics
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: 1007 - 1018 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)