Dynamic average consensus over random networks with additive noise

Jing Wang, Nicola Elia

Research output: Chapter in Book/Report/Conference proceedingConference contribution

8 Scopus citations

Abstract

In this paper, we consider distributed dynamic average consensus problem in the presence of uncertainties on information exchange. Two categories of noise are used to characterize these uncertainties: the first is multiplicative noise that captures the randomness of network connections, while the second is additive noise that describes several uncertainty sources. We propose an iterative algorithm that allows each agent to compute/track the average of their private dynamic signals in the presence of both kinds of noise. This algorithm relaxes restrictive assumptions on consensus over random directed network topologies, such as doubly stochastic weights, symmetric link switching styles, etc, and introduces new mechanisms for mitigating effects of communication uncertainties on information aggregation.

Original languageEnglish (US)
Title of host publication2010 49th IEEE Conference on Decision and Control, CDC 2010
Pages4789-4794
Number of pages6
DOIs
StatePublished - Dec 1 2010
Externally publishedYes
Event2010 49th IEEE Conference on Decision and Control, CDC 2010 - Atlanta, GA, United States
Duration: Dec 15 2010Dec 17 2010

Publication series

NameProceedings of the IEEE Conference on Decision and Control
ISSN (Print)0191-2216

Other

Other2010 49th IEEE Conference on Decision and Control, CDC 2010
Country/TerritoryUnited States
CityAtlanta, GA
Period12/15/1012/17/10

Keywords

  • Additive noise
  • Consensus
  • Dynamic average consensus
  • Link failures
  • Random networks
  • Sensor fusion

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