Excitation allocation for generic identifiability of linear dynamic networks with fixed modules

Journal Article (2022)
Author(s)

H. J. Dreef (Eindhoven University of Technology)

Shengling Shi (TU Delft - Team Bart De Schutter)

Xiaodong Cheng (University of Cambridge)

M. C.F. Donkers (Eindhoven University of Technology)

P. M.J. Van den Hof (Eindhoven University of Technology)

Research Group
Team Bart De Schutter
Copyright
© 2022 H. J. Dreef, S. Shi, X. Cheng, M. C.F. Donkers, P. M.J. Van den Hof
DOI related publication
https://doi.org/10.1109/LCSYS.2022.3171172
More Info
expand_more
Publication Year
2022
Language
English
Copyright
© 2022 H. J. Dreef, S. Shi, X. Cheng, M. C.F. Donkers, P. M.J. Van den Hof
Research Group
Team Bart De Schutter
Volume number
6
Pages (from-to)
2587-2592
Reuse Rights

Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.

Abstract

Identifiability of linear dynamic networks requires the presence of a sufficient number of external excitation signals. The problem of allocating a minimal number of external signals for guaranteeing generic network identifiability in the full measurement case has been recently addressed in the literature. Here we will extend that work by explicitly incorporating the situation that some network modules are known, and thus are fixed in the parametrized model set. The graphical approach introduced earlier is extended to this situation, showing that the presence of fixed modules reduces the required number of external signals. An algorithm is presented that allocates the external signals in a systematic fashion.

Files

Excitation_Allocation_for_Gene... (pdf)
(pdf | 0.596 Mb)
- Embargo expired in 01-07-2023
License info not available