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Cited by 384

The DOI matches its Crossref record; 24 of the 24 references it cites were found in a registry or an index.

State consensus for multi-agent systems with switching topologies and time-varying delays

Xiao, F., Wang, L.·2006·International Journal of Control

Abstract

In this paper, we investigate state consensus problems for discrete-time multi-agent systems with changing communications topologies and bounded time-varying communication delays. The analysis in this paper is based on the properties of non-negative matrices. We first extend the model of networks of dynamic agents to the case with multiple time-delays and prove that if the communication topology, time-delays, and weighting factors are time-invariant, then the necessary and sufficient condition that the multi-agent system solves a consensus problem is that the communication topology, represented by a directed graph, has spanning trees. Then we allow for dynamically changing communication topologies and bounded time-varying communication delays, and present some sufficient conditions for state consensus of system. Finally, as a special case of our model, the problem of asynchronous information exchange is also discussed.

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Metadata

Authors
Xiao, F., & Wang, L.
Venue
International Journal of Control
Published
2006-10
Volume
79
Issue
10
Pages
1277-1284
ISSN
  • 0020-7179 (ISSN-L)
  • 1366-5820
OpenAlex
W2099586295
Cited by
384
References
24
FWCI
13.67
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: State consensus for multi-agent systems with switching topologies and time-varying delays. https://app.cochat.ai/papers/10.1080/00207170600825097
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Xiao, F., & Wang, L. (2006). State consensus for multi-agent systems with switching topologies and time-varying delays. International Journal of Control. https://doi.org/10.1080/00207170600825097

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.