Work

Identifiability in Dynamic Acyclic Networks With Partial Excitation and Measurement

Year: 2024

Type: article

Abstract: This article deals with dynamic networks in which the causality relations between the vertex signals are represented by linear time-invariant transfer functions (modules). Considering an acyclic network where only a subset of its vertices are measured and a subset of the vertices are excited, we explore conditions under which all the modules are identifiable on the basis of measurement data. Two sufficient conditions are presented, where the first condition concerns an identifiability analysis that needs to be performed for each vertex, while the second condition, based on the concept oftree/antitree covering, results from a graphical synthesis approach to allocate actuators and sensors in acyclic networks for achieving generic identifiability.

Source: IEEE Transactions on Automatic Control

DOI: 10.1109/tac.2024.3476989

Authors: Xiaodong Cheng, Shengling Shi, Ioannis Lestas, Paul M.J. Van den Hof

Institutions: Wageningen University & Research, Massachusetts Institute of Technology, University of Cambridge, Eindhoven University of Technology

Language: English

Topic: Control Systems and Identification

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: green

Funder: H2020 European Research Council

Awards: Data-driven Modelling in Dynamic Networks, Analysis and control of large scale heterogeneous networks: scalability, robustness and fundamental limits