Identifiability of diffusively coupled linear networks with partial instrumentation

E.M.M. (Lizan) Kivits; Paul M.J. Van den Hof

doi:10.1016/j.ifacol.2023.10.1213

Identifiability of diffusively coupled linear networks with partial instrumentation

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Abstract

This paper presents identifiability conditions for identifying the complete dynamics of diffusively coupled linear networks. These conditions are derived by exploiting the uniqueness of the nonmonic polynomial network description, given the locations of the actuators and sensors. The analysis is performed under a more relaxed instrumentation setup than the typical restriction to a full set of sensors (full measurement) or a full set of actuators (full excitation). This leads to more general identifiability conditions, including more flexible instrumentation requirements.

Original language	English
Pages (from-to)	2395-2400
Number of pages	6
Journal	IFAC-PapersOnLine
Volume	56
Issue number	2
DOIs	https://doi.org/10.1016/j.ifacol.2023.10.1213
Publication status	Published - 1 Jul 2023
Event	22nd World Congress of the International Federation of Automatic Control (IFAC 2023 World Congress) - Yokohama, Japan Duration: 9 Jul 2023 → 14 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

This project has received funding from the European Research Council (ERC), Advanced Research Grant SYSDYNET, under the European Union's Horizon 2020 research and innovation programme (grant agreement No 694504).

Funders	Funder number
European Union's Horizon 2020 - Research and Innovation Framework Programme
European Union's Horizon 2020 - Research and Innovation Framework Programme	694504

Keywords

Identifiability
diffusive couplings
experiment design
linear dynamic networks

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10.1016/j.ifacol.2023.10.1213

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title = "Identifiability of diffusively coupled linear networks with partial instrumentation",

abstract = "This paper presents identifiability conditions for identifying the complete dynamics of diffusively coupled linear networks. These conditions are derived by exploiting the uniqueness of the nonmonic polynomial network description, given the locations of the actuators and sensors. The analysis is performed under a more relaxed instrumentation setup than the typical restriction to a full set of sensors (full measurement) or a full set of actuators (full excitation). This leads to more general identifiability conditions, including more flexible instrumentation requirements.",

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AU - Van den Hof, Paul M.J.

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Y1 - 2023/7/1

N2 - This paper presents identifiability conditions for identifying the complete dynamics of diffusively coupled linear networks. These conditions are derived by exploiting the uniqueness of the nonmonic polynomial network description, given the locations of the actuators and sensors. The analysis is performed under a more relaxed instrumentation setup than the typical restriction to a full set of sensors (full measurement) or a full set of actuators (full excitation). This leads to more general identifiability conditions, including more flexible instrumentation requirements.

AB - This paper presents identifiability conditions for identifying the complete dynamics of diffusively coupled linear networks. These conditions are derived by exploiting the uniqueness of the nonmonic polynomial network description, given the locations of the actuators and sensors. The analysis is performed under a more relaxed instrumentation setup than the typical restriction to a full set of sensors (full measurement) or a full set of actuators (full excitation). This leads to more general identifiability conditions, including more flexible instrumentation requirements.

KW - Identifiability

KW - diffusive couplings

KW - experiment design

KW - linear dynamic networks

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DO - 10.1016/j.ifacol.2023.10.1213

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T2 - 22nd World Congress of the International Federation of Automatic Control (IFAC 2023 World Congress)

Y2 - 9 July 2023 through 14 July 2023

ER -

Identifiability of diffusively coupled linear networks with partial instrumentation

Abstract

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