Identification in dynamic networks

P.M.J. van den Hof, A.G. Dankers, H.H.M. Weerts

Research output: Contribution to journalArticleAcademicpeer-review

8 Citations (Scopus)
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System identification is a common tool for estimating (linear) plant models as a basis for model-based predictive control and optimization. The current challenges in process industry, however, ask for data-driven modelling techniques that go beyond the single unit/plant models. While optimization and control problems become more and more structured in the form of decentralized and/or distributed solutions, the related modelling problems will need to address structured and interconnected systems. An introduction will be given to the current state of the art and related developments in the identification of linear dynamic networks. Starting from classical prediction error methods for open-loop and closed-loop systems, several consequences for the handling of network situations will be presented and new research questions will be highlighted.

Original languageEnglish
Pages (from-to)23-29
Number of pages7
JournalComputers and Chemical Engineering
Publication statusPublished - 4 Jan 2018
EventFoundations of Computer Aided Process Operations / Chemical Process Control FOCAPO/CPC 2017 - Tucson, United States
Duration: 8 Jan 201712 Jan 2017


  • Closed-loop identification
  • Distributed control
  • Dynamic networks
  • Experiment design
  • Identifiability
  • Model-based control
  • System identification


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