Adaptive consensus control of coopetition networks with high-order agent dynamics

Yanzhi Wu; Jiangping Hu; Lixin Gao; Bijoy K. Ghosh

doi:10.1109/ASCC.2017.8287432

Adaptive consensus control of coopetition networks with high-order agent dynamics

Yanzhi Wu, Jiangping Hu, Lixin Gao, Bijoy K. Ghosh

Mathematics and Statistics

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

In this paper, we consider consensus control of high-order multi-agent systems with antagonistic interactions and unknown disturbances. The interaction topology associated with the multi-agent system is described by a coopetition network. Linearly parameterized approaches are used to model the unknown disturbances. Some novel transformations are presented to design the distributed adaptive controllers, which guarantees that a bipartite consensus can be achieved for all the agents. At the same time, convergence of the bipartite consensus error is analyzed with the help of signed graph theory and the Barbalat's Lemma. Some simulation results are also provided to demonstrate the effectiveness of the proposed adaptive control strategy.

Original language	English
Title of host publication	2017 Asian Control Conference, ASCC 2017
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1713-1718
Number of pages	6
ISBN (Electronic)	9781509015733
DOIs	https://doi.org/10.1109/ASCC.2017.8287432
State	Published - Feb 7 2018
Event	2017 11th Asian Control Conference, ASCC 2017 - Gold Coast, Australia Duration: Dec 17 2017 → Dec 20 2017

Publication series

Name	2017 Asian Control Conference, ASCC 2017
Volume	2018-January

Conference

Conference	2017 11th Asian Control Conference, ASCC 2017
Country/Territory	Australia
City	Gold Coast
Period	12/17/17 → 12/20/17

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10.1109/ASCC.2017.8287432

Cite this

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title = "Adaptive consensus control of coopetition networks with high-order agent dynamics",

abstract = "In this paper, we consider consensus control of high-order multi-agent systems with antagonistic interactions and unknown disturbances. The interaction topology associated with the multi-agent system is described by a coopetition network. Linearly parameterized approaches are used to model the unknown disturbances. Some novel transformations are presented to design the distributed adaptive controllers, which guarantees that a bipartite consensus can be achieved for all the agents. At the same time, convergence of the bipartite consensus error is analyzed with the help of signed graph theory and the Barbalat's Lemma. Some simulation results are also provided to demonstrate the effectiveness of the proposed adaptive control strategy.",

author = "Yanzhi Wu and Jiangping Hu and Lixin Gao and Ghosh, {Bijoy K.}",

note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 2017 11th Asian Control Conference, ASCC 2017 ; Conference date: 17-12-2017 Through 20-12-2017",

year = "2018",

month = feb,

day = "7",

doi = "10.1109/ASCC.2017.8287432",

language = "English",

series = "2017 Asian Control Conference, ASCC 2017",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "1713--1718",

booktitle = "2017 Asian Control Conference, ASCC 2017",

}

Wu, Y, Hu, J, Gao, L & Ghosh, BK 2018, Adaptive consensus control of coopetition networks with high-order agent dynamics. in 2017 Asian Control Conference, ASCC 2017. 2017 Asian Control Conference, ASCC 2017, vol. 2018-January, Institute of Electrical and Electronics Engineers Inc., pp. 1713-1718, 2017 11th Asian Control Conference, ASCC 2017, Gold Coast, Australia, 12/17/17. https://doi.org/10.1109/ASCC.2017.8287432

Adaptive consensus control of coopetition networks with high-order agent dynamics. / Wu, Yanzhi; Hu, Jiangping; Gao, Lixin et al.
2017 Asian Control Conference, ASCC 2017. Institute of Electrical and Electronics Engineers Inc., 2018. p. 1713-1718 (2017 Asian Control Conference, ASCC 2017; Vol. 2018-January).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Adaptive consensus control of coopetition networks with high-order agent dynamics

AU - Wu, Yanzhi

AU - Hu, Jiangping

AU - Gao, Lixin

AU - Ghosh, Bijoy K.

PY - 2018/2/7

Y1 - 2018/2/7

N2 - In this paper, we consider consensus control of high-order multi-agent systems with antagonistic interactions and unknown disturbances. The interaction topology associated with the multi-agent system is described by a coopetition network. Linearly parameterized approaches are used to model the unknown disturbances. Some novel transformations are presented to design the distributed adaptive controllers, which guarantees that a bipartite consensus can be achieved for all the agents. At the same time, convergence of the bipartite consensus error is analyzed with the help of signed graph theory and the Barbalat's Lemma. Some simulation results are also provided to demonstrate the effectiveness of the proposed adaptive control strategy.

AB - In this paper, we consider consensus control of high-order multi-agent systems with antagonistic interactions and unknown disturbances. The interaction topology associated with the multi-agent system is described by a coopetition network. Linearly parameterized approaches are used to model the unknown disturbances. Some novel transformations are presented to design the distributed adaptive controllers, which guarantees that a bipartite consensus can be achieved for all the agents. At the same time, convergence of the bipartite consensus error is analyzed with the help of signed graph theory and the Barbalat's Lemma. Some simulation results are also provided to demonstrate the effectiveness of the proposed adaptive control strategy.

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U2 - 10.1109/ASCC.2017.8287432

DO - 10.1109/ASCC.2017.8287432

M3 - Conference contribution

AN - SCOPUS:85047446117

T3 - 2017 Asian Control Conference, ASCC 2017

SP - 1713

EP - 1718

BT - 2017 Asian Control Conference, ASCC 2017

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2017 11th Asian Control Conference, ASCC 2017

Y2 - 17 December 2017 through 20 December 2017

ER -

Adaptive consensus control of coopetition networks with high-order agent dynamics

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