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The DOI matches its Crossref record; 34 of the 34 references it cites were found in a registry or an index.

Consensus of Hybrid Multi-Agent Systems

Open access
Zheng, Yuanshi, Ma, Jingying, Wang, Long·2018·IEEE Transactions on Neural Networks and Learning Systems

Abstract

In this brief, we consider the consensus problem of hybrid multiagent systems. First, the hybrid multiagent system is proposed, which is composed of continuous-time and discrete-time dynamic agents. Then, three kinds of consensus protocols are presented for the hybrid multiagent system. The analysis tool developed in this brief is based on the matrix theory and graph theory. With different restrictions of the sampling period, some necessary and sufficient conditions are established for solving the consensus of the hybrid multiagent system. The consensus states are also obtained under different protocols. Finally, simulation examples are provided to demonstrate the effectiveness of our theoretical results.

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Metadata

Authors
Zheng, Y., Ma, J., & Wang, L.
Venue
IEEE Transactions on Neural Networks and Learning Systems
Published
2018-04
Volume
29
Issue
4
Pages
1359-1365
ISSN
  • 2162-237X (ISSN-L)
  • 2162-2388
OpenAlex
W2963252619
Cited by
391
References
34
FWCI
37.61
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Consensus of Hybrid Multi-Agent Systems. https://app.cochat.ai/papers/10.1109/tnnls.2017.2651402
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Zheng, Y., Ma, J., & Wang, L. (2018). Consensus of Hybrid Multi-Agent Systems. IEEE Transactions on Neural Networks and Learning Systems. https://doi.org/10.1109/tnnls.2017.2651402

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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.