Distributed Optimisation with Stochastic Event-Triggered Multi-Agent Control Algorithm

Kam Fai Elvis Tsang; Junfeng Wu; Ling Shi

doi:10.1109/CDC42340.2020.9303799

Distributed Optimisation with Stochastic Event-Triggered Multi-Agent Control Algorithm

Kam Fai Elvis Tsang, Junfeng Wu, Ling Shi

Department of Electronic and Computer Engineering

Research output: Chapter in Book/Conference Proceeding/Report › Conference Paper published in a book › peer-review

2 Citations (Scopus)

Abstract

In this paper, we study the distributed optimisation problem in which multiple agents cooperatively and distributively solve an optimisation problem. In order to avoid continuous communication among agents, we propose a stochastic distributed dynamic event-triggering law to schedule the communication. We show that the optimisation problem can be solved with exponential rate and arbitrarily small optimisation error. We further prove that Zeno behaviour does not exist in the proposed stochastic event-triggering law by constructing a lower bound on the inter-event interval which is essential for the feasibility of proposed algorithm. A numerical simulation is presented to illustrate the effectiveness of the proposed algorithm when compared with some existing event-triggered distributed optimisation algorithms.

Original language	English
Title of host publication	2020 59th IEEE Conference on Decision and Control, CDC 2020
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	6222-6227
Number of pages	6
ISBN (Electronic)	9781728174471
DOIs	https://doi.org/10.1109/CDC42340.2020.9303799
Publication status	Published - 14 Dec 2020
Event	59th IEEE Conference on Decision and Control, CDC 2020 - Virtual, Jeju Island, Korea, Republic of Duration: 14 Dec 2020 → 18 Dec 2020

Publication series

Name	Proceedings of the IEEE Conference on Decision and Control
Volume	2020-December
ISSN (Print)	0743-1546
ISSN (Electronic)	2576-2370

Conference

Conference	59th IEEE Conference on Decision and Control, CDC 2020
Country/Territory	Korea, Republic of
City	Virtual, Jeju Island
Period	14/12/20 → 18/12/20

Bibliographical note

Publisher Copyright:
© 2020 IEEE.

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10.1109/CDC42340.2020.9303799

Cite this

Elvis Tsang, K. F., Wu, J., & Shi, L. (2020). Distributed Optimisation with Stochastic Event-Triggered Multi-Agent Control Algorithm. In 2020 59th IEEE Conference on Decision and Control, CDC 2020 (pp. 6222-6227). Article 9303799 (Proceedings of the IEEE Conference on Decision and Control; Vol. 2020-December). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CDC42340.2020.9303799

@inproceedings{b7c7ae51fdd74da6a2797512a1ba930f,

title = "Distributed Optimisation with Stochastic Event-Triggered Multi-Agent Control Algorithm",

abstract = "In this paper, we study the distributed optimisation problem in which multiple agents cooperatively and distributively solve an optimisation problem. In order to avoid continuous communication among agents, we propose a stochastic distributed dynamic event-triggering law to schedule the communication. We show that the optimisation problem can be solved with exponential rate and arbitrarily small optimisation error. We further prove that Zeno behaviour does not exist in the proposed stochastic event-triggering law by constructing a lower bound on the inter-event interval which is essential for the feasibility of proposed algorithm. A numerical simulation is presented to illustrate the effectiveness of the proposed algorithm when compared with some existing event-triggered distributed optimisation algorithms.",

author = "\{Elvis Tsang\}, \{Kam Fai\} and Junfeng Wu and Ling Shi",

note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 59th IEEE Conference on Decision and Control, CDC 2020 ; Conference date: 14-12-2020 Through 18-12-2020",

year = "2020",

month = dec,

day = "14",

doi = "10.1109/CDC42340.2020.9303799",

language = "English",

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booktitle = "2020 59th IEEE Conference on Decision and Control, CDC 2020",

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Elvis Tsang, KF, Wu, J & Shi, L 2020, Distributed Optimisation with Stochastic Event-Triggered Multi-Agent Control Algorithm. in 2020 59th IEEE Conference on Decision and Control, CDC 2020., 9303799, Proceedings of the IEEE Conference on Decision and Control, vol. 2020-December, Institute of Electrical and Electronics Engineers Inc., pp. 6222-6227, 59th IEEE Conference on Decision and Control, CDC 2020, Virtual, Jeju Island, Korea, Republic of, 14/12/20. https://doi.org/10.1109/CDC42340.2020.9303799

Distributed Optimisation with Stochastic Event-Triggered Multi-Agent Control Algorithm. / Elvis Tsang, Kam Fai; Wu, Junfeng; Shi, Ling.
2020 59th IEEE Conference on Decision and Control, CDC 2020. Institute of Electrical and Electronics Engineers Inc., 2020. p. 6222-6227 9303799 (Proceedings of the IEEE Conference on Decision and Control; Vol. 2020-December).

Research output: Chapter in Book/Conference Proceeding/Report › Conference Paper published in a book › peer-review

TY - GEN

T1 - Distributed Optimisation with Stochastic Event-Triggered Multi-Agent Control Algorithm

AU - Elvis Tsang, Kam Fai

AU - Wu, Junfeng

AU - Shi, Ling

PY - 2020/12/14

Y1 - 2020/12/14

N2 - In this paper, we study the distributed optimisation problem in which multiple agents cooperatively and distributively solve an optimisation problem. In order to avoid continuous communication among agents, we propose a stochastic distributed dynamic event-triggering law to schedule the communication. We show that the optimisation problem can be solved with exponential rate and arbitrarily small optimisation error. We further prove that Zeno behaviour does not exist in the proposed stochastic event-triggering law by constructing a lower bound on the inter-event interval which is essential for the feasibility of proposed algorithm. A numerical simulation is presented to illustrate the effectiveness of the proposed algorithm when compared with some existing event-triggered distributed optimisation algorithms.

AB - In this paper, we study the distributed optimisation problem in which multiple agents cooperatively and distributively solve an optimisation problem. In order to avoid continuous communication among agents, we propose a stochastic distributed dynamic event-triggering law to schedule the communication. We show that the optimisation problem can be solved with exponential rate and arbitrarily small optimisation error. We further prove that Zeno behaviour does not exist in the proposed stochastic event-triggering law by constructing a lower bound on the inter-event interval which is essential for the feasibility of proposed algorithm. A numerical simulation is presented to illustrate the effectiveness of the proposed algorithm when compared with some existing event-triggered distributed optimisation algorithms.

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UR - https://www.scopus.com/pages/publications/85099875928

U2 - 10.1109/CDC42340.2020.9303799

DO - 10.1109/CDC42340.2020.9303799

M3 - Conference Paper published in a book

T3 - Proceedings of the IEEE Conference on Decision and Control

SP - 6222

EP - 6227

BT - 2020 59th IEEE Conference on Decision and Control, CDC 2020

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 59th IEEE Conference on Decision and Control, CDC 2020

Y2 - 14 December 2020 through 18 December 2020

ER -

Distributed Optimisation with Stochastic Event-Triggered Multi-Agent Control Algorithm

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