On robustness and leadership in Markov switching consensus networks

Sarah Huiyi Cen, Vaibhav Srivastava, Naomi Ehrich Leonard

Research output: Chapter in Book/Report/Conference proceedingConference 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 languageEnglish (US)
Title of host publication2017 IEEE 56th Annual Conference on Decision and Control, CDC 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1701-1706
Number of pages6
ISBN (Electronic)9781509028733
DOIs
StatePublished - Jan 18 2018
Event56th IEEE Annual Conference on Decision and Control, CDC 2017 - Melbourne, Australia
Duration: Dec 12 2017Dec 15 2017

Publication series

Name2017 IEEE 56th Annual Conference on Decision and Control, CDC 2017
Volume2018-January

Other

Other56th IEEE Annual Conference on Decision and Control, CDC 2017
CountryAustralia
CityMelbourne
Period12/12/1712/15/17

All Science Journal Classification (ASJC) codes

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

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