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  5. Consensus of multi‐agent systems with nonlinear dynamics and sampled‐data information: a delayed‐input approach

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Article
English
2012

Consensus of multi‐agent systems with nonlinear dynamics and sampled‐data information: a delayed‐input approach

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English
2012
International Journal of Robust and Nonlinear Control
Vol 23 (6)
DOI: 10.1002/rnc.2779

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Guanrong Chen
Guanrong Chen

City University Of Hong Kong

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Guanghui Wen
Zhisheng Duan
Wenwu Yu
+1 more

Abstract

SUMMARY This paper is concerned with the problem of consensus in directed networks of multiple agents with intrinsic nonlinear dynamics and sampled‐data information. A new protocol is induced from a class of continuous‐time linear consensus protocols by implementing data‐sampling technique and a zero‐order hold circuit. On the basis of a delayed‐input approach, the sampled‐data multi‐agent system is converted to an equivalent nonlinear system with a time‐varying delay. Theoretical analysis on this time‐delayed system shows that consensus with asymptotic time‐varying velocities in a strongly connected network can be achieved over some suitable sampled‐data intervals. A multi‐step procedure is further presented to estimate the upper bound of the maximal allowable sampling intervals. The results are then extended to a network topology with a directed spanning tree. For the case of the topology without a directed spanning tree, it is shown that the new protocol can still guarantee the system to achieve consensus by appropriately informing a fraction of agents. Finally, some numerical simulations are presented to demonstrate the effectiveness of the theoretical results and the dependence of the upper bound of maximal allowable sampling interval on the coupling strength. Copyright © 2012 John Wiley & Sons, Ltd.

How to cite this publication

Guanghui Wen, Zhisheng Duan, Wenwu Yu, Guanrong Chen (2012). Consensus of multi‐agent systems with nonlinear dynamics and sampled‐data information: a delayed‐input approach. International Journal of Robust and Nonlinear Control, 23(6), pp. 602-619, DOI: 10.1002/rnc.2779.

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Publication Details

Type

Article

Year

2012

Authors

4

Datasets

0

Total Files

0

Language

English

Journal

International Journal of Robust and Nonlinear Control

DOI

10.1002/rnc.2779

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