Clustering-based average state observer design for large-scale network systems

doi:10.1016/j.automatica.2023.110914

Clustering-based average state observer design for large-scale network systems

2023-05-01

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OA

AI

M

Muhammad Umar B. Niazi

X

Xiaodong Cheng *

C

Carlos Canudas de Wit

J

Jacquelien M.A. Scherpen

DOI：10.1016/j.automatica.2023.110914

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Abstract

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This paper addresses the aggregated monitoring problem for large-scale network systems with a few dedicated sensors. Full state estimation of such systems is often infeasible due to unobservability and/or computational infeasibility; therefore, through clustering and aggregation, a tractable representation of a network system, called a projected network system, is obtained for designing a minimum-order average state observer. This observer estimates the average states of the clusters, which are identified under explicit consideration of estimation error. Moreover, given the clustering, the proposed observer design algorithm exploits the structure of the estimation error dynamics to achieve computational tractability. Simulations show that the computation of the proposed algorithm is significantly faster than the usual H2/H infinity observer design techniques. On the other hand, compromise on the estimation error characteristics is shown to be marginal.(c) 2023 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

Keywords:

Large-scale systems

Network clustering

Observer design

Computational complexity

Clustering-based average state observer design for large-scale network systems

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