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

Gusrialdi, Azwirman; Qu, Zhihua

doi:10.1016/j.ifacol.2020.12.048

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

Conference · Wed Jan 01 04:00:00 EST 2020 · IFAC-PapersOnLine

DOI:https://doi.org/10.1016/j.ifacol.2020.12.048· OSTI ID:1821438

Gusrialdi, Azwirman ^[1]; Qu, Zhihua

University of central Florida

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.

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Research Organization:: National Renewable Energy Laboratory

Sponsoring Organization:: USDOE

DOE Contract Number:: EE0007998; EE0007327

OSTI ID:: 1821438

Conference Information:: Journal Name: IFAC-PapersOnLine Journal Issue: 2 Journal Volume: 53

Country of Publication:: United States

Language:: English

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References (19)

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