Data-Driven Distributed Algorithms for Estimating Eigenvalues and Eigenvectors of Interconnected Dynamical Systems

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Abstract

The paper presents data-driven algorithms to estimate in a distributed manner the eigenvalues, right and left eigenvectors of an unknown linear (or linearized) interconnected dynamic system. In particular, the proposed algorithms do not require the identification of the system model in advance before performing the estimation. As a first step, we consider interconnected dynamical system with distinct eigenvalues. The proposed strategy first estimates the eigenvalues using the well-known Prony method. The right and left eigenvectors are then estimated by solving distributively a set of linear equations. One important feature of the proposed algorithms is that the topology of communication network used to perform the distributed estimation can be chosen arbitrarily, given that it is connected, and is also independent of the structure or sparsity of the system (state) matrix. The proposed distributed algorithms are demonstrated via a numerical example.
Original languageEnglish
Title of host publication21th IFAC World Congress
EditorsRolf Findeisen, Sandra Hirche, Klaus Janschek, Martin Mönnigmann
PublisherElsevier
Pages52-57
Number of pages6
DOIs
Publication statusPublished - 2020
Publication typeA4 Article in a conference publication
EventIFAC World Congress - Berlin, Germany
Duration: 11 Jul 202017 Jul 2020

Publication series

NameIFAC-PapersOnLine
PublisherElsevier
Number2
Volume53
ISSN (Print)2405-8971
ISSN (Electronic)2405-8963

Conference

ConferenceIFAC World Congress
Country/TerritoryGermany
CityBerlin
Period11/07/2017/07/20

Publication forum classification

  • Publication forum level 1

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