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A decentralized asymmetric weighting approach for improved convergence of multi-agent systems with undirected interaction
Publikationstyp
Conference Paper
Date Issued
2014-08
Sprache
English
Author(s)
Institut
Journal
Volume
19
Start Page
8317
End Page
8322
Citation
IFAC Proceedings Volumes (IFAC-PapersOnline) 19: 8317-8322 (2014)
Contribution to Conference
Publisher DOI
Scopus ID
Publisher
Elsevier
This work considers the convergence rate of multi-agent systems with discrete-time single-integrator dynamics and undirected interaction topologies. In recent work it has been proven that in case of lattice interaction topologies the convergence rate can be bounded away from zero, independent of the network size, using asymmetric weightings that give the interaction graph a preferred communication direction. Approximation methods for more general graphs, based on relative angles between agents, are presented, which suggest that the convergence rate is bounded away from zero as well. This work proposes alternative approximation methods, that improve the convergence rate compared to previous approximation methods. Furthermore it is shown that the improvement of the approximation methods degrade in comparison to other weighting approaches, the more the considered topology differs from a lattice graph. Therefore an iterative algorithm is proposed, that extends the idea of a preferred communication direction to general graphs, which are not similar to lattices or where the relative angles are not known.
Subjects
Convergence
Decentralized systems
Graph theory
Multi-agent system
Networks
DDC Class
004: Informatik