Identification of dynamic models in complex networks with Prediction Error Methods: Predictor Input Selection

Arne Dankers, Paul M J Van Den Hof, Xavier Bombois, Peter S C Heuberger

Research output: Contribution to journalArticleScientificpeer-review

41 Citations (Scopus)

Abstract

This paper addresses the problem of obtaining an estimate of a particular module of interest that is embedded in a dynamic network with known interconnection structure. In this paper it is shown that there is considerable freedom as to which variables can be included as inputs to the predictor, while still obtaining consistent estimates of the particular module of interest. This freedom is encoded into sufficient conditions on the set of predictor inputs that allow for consistent identification of the module. The conditions can be used to design a sensor placement scheme, or to determine whether it is possible to obtain consistent estimates while refraining from measuring particular variables in the network. As identification methods the Direct and Two Stage Prediction-Error methods are considered. Algorithms are presented for checking the conditions using tools from graph theory.

Original languageEnglish
Pages (from-to)937-952
JournalIEEE Transactions on Automatic Control
Volume61
Issue number4
DOIs
Publication statusPublished - 2016

Keywords

  • Closed-loop identification
  • dynamic networks
  • graph theory
  • linear systems
  • system identification

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