Single module identifiability in linear dynamic networks with partial excitation and measurement

Shengling Shi, Xiaodong Cheng, Paul M.J. Van den Hof

Research output: Contribution to journalArticleScientificpeer-review

Abstract

Identifiability of a single module in a network of transfer functions is determined by the question whether a particular transfer function in the network can be uniquely distinguished within a network model set, on the basis of data. Whereas previous research has focused on the situations that all network signals are either excited or measured, we develop generalized analysis results for the situation of partial measurement and partial excitation. As identifiability conditions typically require a sufficient number of external excitation signals, this work introduces a novel network model structure such that excitation from unmeasured noise signals is included, which leads to less conservative identifiability conditions than relying on measured excitation signals only. More importantly, graphical conditions are developed to verify global and generic identifiability of a single module based on the topology of the dynamic network. Depending on whether the input or the output of the module can be measured, we present four identifiability conditions which cover all possible situations in single module identification. These conditions further lead to synthesis approaches

Original languageEnglish
Number of pages16
JournalIEEE Transactions on Automatic Control
DOIs
Publication statusAccepted/In press - 2021

Keywords

  • Brain modeling
  • Data models
  • dynamic networks
  • graph theory
  • identifiability
  • MISO communication
  • Network topology
  • Power system dynamics
  • System identification
  • Topology
  • Transfer functions

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