Signal selection for local module identification in linear dynamic networks: A graphical approach

Shengling Shi, Xiaodong Cheng (Corresponding author), Bart De Schutter, Paul M.J. Van den Hof

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Abstract

In a dynamic network of interconnected transfer functions, it is not necessary to use all the node signals for estimating a local transfer function. Given the network topology, detailed conditions are available for selecting inputs and outputs in a (MIMO) predictor model that warrants consistent and minimum variance estimation of a target module through the so-called local direct method. Motivated by the existing minimum-input signal selection approach that gradually incorporates additional signals, an alternative graphical algorithm for signal selection is developed in this work by directly exploiting the complete network graph. Then, as a straightforward application of existing analytical results, graphical conditions for consistent identification are derived for the novel signal selection approach. We show by an example that in some cases, for the consistent estimation of the target module, the developed method leads to fewer selected signals than the original minimum-input method.

Original languageEnglish
Pages (from-to)2407-2412
Number of pages6
JournalIFAC-PapersOnLine
Volume56
Issue number2
DOIs
Publication statusPublished - Nov 2023
Event22nd World Congress of the International Federation of Automatic Control (IFAC 2023 World Congress) - Yokohama, Japan
Duration: 9 Jul 202314 Jul 2023
Conference number: 22
https://www.ifac2023.org/

Bibliographical note

Publisher Copyright:
Copyright © 2023 The Authors. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/)

Funding

FundersFunder number
Horizon 2020 Framework Programme
European Research Councilao2021

    Keywords

    • dynamic networks
    • identifiability
    • interconnected systems
    • System identification

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