Abstractions of linear dynamic networks for input selection in local module identification

Harm H.M. Weerts, Jonas Linder, Martin Enqvist, Paul M.J. Van den Hof (Corresponding author)

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

In abstractions of linear dynamic networks, selected node signals are removed from the network, while keeping the remaining node signals invariant. The topology and link dynamics, or modules, of an abstracted network will generally be changed compared to the original network. Abstractions of dynamic networks can be used to select an appropriate set of node signals that are to be measured, on the basis of which a particular local module can be estimated. A method is introduced for network abstraction that generalizes previously introduced algorithms, as e.g. immersion and the method of indirect inputs. For this abstraction method it is shown under which conditions on the selected signals a particular module will remain invariant. This leads to sets of conditions on selected measured node variables that allow identification of the target module.

Original languageEnglish
Article number108975
JournalAutomatica
Volume117
DOIs
Publication statusPublished - Jul 2020

Keywords

  • Closed-loop identification
  • Dynamic networks
  • Graph theory
  • System identification

Fingerprint Dive into the research topics of 'Abstractions of linear dynamic networks for input selection in local module identification'. Together they form a unique fingerprint.

  • Cite this