On robustness and leadership in Markov switching consensus networks

Sarah Huiyi Cen; Vaibhav Srivastava; Naomi Ehrich Leonard

doi:10.1109/CDC.2017.8263895

On robustness and leadership in Markov switching consensus networks

Sarah Huiyi Cen, Vaibhav Srivastava, Naomi Ehrich Leonard

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

1 Scopus citations

Abstract

We examine the influence of time-varying interactions, which are modeled by a Markov switching graph (MSG), on noisy multi-agent dynamics. Our focus is on the robustness of both consensus and leader-follower tracking dynamics in the presence of stochastic noise, and we derive expressions for the steady-state covariance of the system's deviation from consensus and tracking error, respectively. We use these measures to quantify individual and group performance as functions of the interaction graphs and graph switching matrix. We extend notions of robustness and joint centrality indices for static graphs to MSGs.

Original language	English (US)
Title of host publication	2017 IEEE 56th Annual Conference on Decision and Control, CDC 2017
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1701-1706
Number of pages	6
ISBN (Electronic)	9781509028733
DOIs	https://doi.org/10.1109/CDC.2017.8263895
State	Published - Jun 28 2017
Event	56th IEEE Annual Conference on Decision and Control, CDC 2017 - Melbourne, Australia Duration: Dec 12 2017 → Dec 15 2017

Publication series

Name	2017 IEEE 56th Annual Conference on Decision and Control, CDC 2017
Volume	2018-January

Other

Other	56th IEEE Annual Conference on Decision and Control, CDC 2017
Country/Territory	Australia
City	Melbourne
Period	12/12/17 → 12/15/17

All Science Journal Classification (ASJC) codes

Decision Sciences (miscellaneous)
Industrial and Manufacturing Engineering
Control and Optimization

Access to Document

10.1109/CDC.2017.8263895

Cite this

Cen, S. H., Srivastava, V., & Leonard, N. E. (2017). On robustness and leadership in Markov switching consensus networks. In 2017 IEEE 56th Annual Conference on Decision and Control, CDC 2017 (pp. 1701-1706). (2017 IEEE 56th Annual Conference on Decision and Control, CDC 2017; Vol. 2018-January). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CDC.2017.8263895

@inproceedings{84e4c0e12bfb47dea4bfd74e0e11c2a1,

title = "On robustness and leadership in Markov switching consensus networks",

abstract = "We examine the influence of time-varying interactions, which are modeled by a Markov switching graph (MSG), on noisy multi-agent dynamics. Our focus is on the robustness of both consensus and leader-follower tracking dynamics in the presence of stochastic noise, and we derive expressions for the steady-state covariance of the system's deviation from consensus and tracking error, respectively. We use these measures to quantify individual and group performance as functions of the interaction graphs and graph switching matrix. We extend notions of robustness and joint centrality indices for static graphs to MSGs.",

author = "Cen, {Sarah Huiyi} and Vaibhav Srivastava and Leonard, {Naomi Ehrich}",

note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 56th IEEE Annual Conference on Decision and Control, CDC 2017 ; Conference date: 12-12-2017 Through 15-12-2017",

year = "2017",

month = jun,

day = "28",

doi = "10.1109/CDC.2017.8263895",

language = "English (US)",

series = "2017 IEEE 56th Annual Conference on Decision and Control, CDC 2017",

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

pages = "1701--1706",

booktitle = "2017 IEEE 56th Annual Conference on Decision and Control, CDC 2017",

address = "United States",

}

Cen, SH, Srivastava, V & Leonard, NE 2017, On robustness and leadership in Markov switching consensus networks. in 2017 IEEE 56th Annual Conference on Decision and Control, CDC 2017. 2017 IEEE 56th Annual Conference on Decision and Control, CDC 2017, vol. 2018-January, Institute of Electrical and Electronics Engineers Inc., pp. 1701-1706, 56th IEEE Annual Conference on Decision and Control, CDC 2017, Melbourne, Australia, 12/12/17. https://doi.org/10.1109/CDC.2017.8263895

On robustness and leadership in Markov switching consensus networks. / Cen, Sarah Huiyi; Srivastava, Vaibhav; Leonard, Naomi Ehrich.
2017 IEEE 56th Annual Conference on Decision and Control, CDC 2017. Institute of Electrical and Electronics Engineers Inc., 2017. p. 1701-1706 (2017 IEEE 56th Annual Conference on Decision and Control, CDC 2017; Vol. 2018-January).

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

TY - GEN

T1 - On robustness and leadership in Markov switching consensus networks

AU - Cen, Sarah Huiyi

AU - Srivastava, Vaibhav

AU - Leonard, Naomi Ehrich

PY - 2017/6/28

Y1 - 2017/6/28

N2 - We examine the influence of time-varying interactions, which are modeled by a Markov switching graph (MSG), on noisy multi-agent dynamics. Our focus is on the robustness of both consensus and leader-follower tracking dynamics in the presence of stochastic noise, and we derive expressions for the steady-state covariance of the system's deviation from consensus and tracking error, respectively. We use these measures to quantify individual and group performance as functions of the interaction graphs and graph switching matrix. We extend notions of robustness and joint centrality indices for static graphs to MSGs.

AB - We examine the influence of time-varying interactions, which are modeled by a Markov switching graph (MSG), on noisy multi-agent dynamics. Our focus is on the robustness of both consensus and leader-follower tracking dynamics in the presence of stochastic noise, and we derive expressions for the steady-state covariance of the system's deviation from consensus and tracking error, respectively. We use these measures to quantify individual and group performance as functions of the interaction graphs and graph switching matrix. We extend notions of robustness and joint centrality indices for static graphs to MSGs.

UR - http://www.scopus.com/inward/record.url?scp=85046285249&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85046285249&partnerID=8YFLogxK

U2 - 10.1109/CDC.2017.8263895

DO - 10.1109/CDC.2017.8263895

M3 - Conference contribution

AN - SCOPUS:85046285249

T3 - 2017 IEEE 56th Annual Conference on Decision and Control, CDC 2017

SP - 1701

EP - 1706

BT - 2017 IEEE 56th Annual Conference on Decision and Control, CDC 2017

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 56th IEEE Annual Conference on Decision and Control, CDC 2017

Y2 - 12 December 2017 through 15 December 2017

ER -

On robustness and leadership in Markov switching consensus networks

Abstract

Publication series

Other

All Science Journal Classification (ASJC) codes

Access to Document

Other files and links

Fingerprint

Cite this