日本語
 
Privacy Policy ポリシー/免責事項
  詳細検索ブラウズ

アイテム詳細

  Data-sampled mean-square consensus of hybrid multi-agent systems with time-varying delay and multiplicative noises

Sun, F., Lu, C., Zhu, W., & Kurths, J. (2023). Data-sampled mean-square consensus of hybrid multi-agent systems with time-varying delay and multiplicative noises. Information Sciences, 624, 674-685. doi:10.1016/j.ins.2022.12.103.

Item is

基本情報

表示: 非表示:
資料種別: 学術論文

ファイル

表示: ファイル

関連URL

表示:

作成者

表示:
非表示:
 作成者:
Sun, Fenglan1, 著者              
Lu, Chuan2, 著者
Zhu, Wei2, 著者
Kurths, Jürgen1, 著者              
所属:
1Potsdam Institute for Climate Impact Research, ou_persistent13              
2External Organizations, ou_persistent22              

内容説明

表示:
非表示:
キーワード: -
 要旨: This paper addresses the issue of dynamic mean-square consensus for second-order hybrid multi-agent systems. Time-varying delays and multiplicative noises are considered. New distributed control protocols are designed based on data-sampled information of neighbor agents. Equivalently using the error system based on Laplacian matrix, the method could make a dynamic consensus both under the fixed and switching topologies. By adopting stochastic system theory, Lyapunov stability method and linear matrix inequality theory, several sufficient conditions for the dynamic mean-square consensus are obtained. The upper bound of time delay and the discrete-time sampling period of hybrid multi-agent systems under a stochastic noises environment are inferred. Several simulations are presented to demonstrate the effectiveness of the proposed methods.

資料詳細

表示:
非表示:
言語: eng - 英語
 日付: 2023-01-092023-05
 出版の状態: Finally published
 ページ: -
 出版情報: -
 目次: -
 査読: 査読あり
 識別子(DOI, ISBNなど): DOI: 10.1016/j.ins.2022.12.103
MDB-ID: No data to archive
PIKDOMAIN: RD4 - Complexity Science
Organisational keyword: RD4 - Complexity Science
Working Group: Network- and machine-learning-based prediction of extreme events
Research topic keyword: Complex Networks
Research topic keyword: Nonlinear Dynamics
Model / method: Machine Learning
 学位: -

関連イベント

表示:

訴訟

表示:

Project information

表示:

出版物 1

表示:
非表示:
出版物名: Information Sciences
種別: 学術雑誌, SCI, Scopus
 著者・編者:
所属:
出版社, 出版地: -
ページ: - 巻号: 624 通巻号: - 開始・終了ページ: 674 - 685 識別子(ISBN, ISSN, DOIなど): CoNE: https://publications.pik-potsdam.de/cone/journals/resource/Information-Sciences
Publisher: Elsevier