Robust dynamic average consensus algorithm for signals with bounded derivatives

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

36 Scopus citations


Development and analysis of a robust dynamic average consensus algorithm for arbitrary reference signals with known bounded derivatives is presented. The proposed approach does not rely on full knowledge of the dynamics generating the reference signals nor assume access to its time derivatives. Compared to existing approaches, the proposed algorithm does not require any initialization criteria and therefore it is robust to changes in network topology. Robustness of the proposed approach is attributed to the two consecutive Laplacian matrices that appear in the algorithm and thus two rounds of communication are needed between each update of the agents' estimates. Therefore we introduce a singularly perturbed system that would effectively place an integral between the consecutive Laplacian matrices and allow the nodes to replace the two rounds of communication involving a single variable with a single round of communication involving two variables. Numerical simulations validate the theoretical contributions of the paper.

Original languageEnglish (US)
Title of host publication2017 American Control Conference, ACC 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)9781509059928
StatePublished - Jun 29 2017
Event2017 American Control Conference, ACC 2017 - Seattle, United States
Duration: May 24 2017May 26 2017

Publication series

NameProceedings of the American Control Conference
ISSN (Print)0743-1619


Other2017 American Control Conference, ACC 2017
Country/TerritoryUnited States

ASJC Scopus subject areas

  • Electrical and Electronic Engineering


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