Abstractions of linear dynamic networks for input selection in local module identification. (July 2020)

• Record Type: Journal Article
• Authors: Weerts, Harm H.M.; Linder, Jonas; Enqvist, Martin; Van den Hof, Paul M.J.
• Is Part Of: Automatica. Volume 117(2020)
• Publication Date: 2020-07
• Subjects: Dynamic networks -- System identification -- Closed-loop identification -- Graph theory; Automatic control -- Periodicals; Automation -- Periodicals; 629.805
• DOI: 10.1016/j.automatica.2020.108975
• Languages: English
• ISSNs: 0005-1098

Abstract

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.