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From Optimal Design of Experiment to Safe System Identification in Type 2 Diabetes

  • Sarah Ellinor Engell*
  • , Henrik Bengtsson
  • , Jeppe Sturis
  • , Dimitri Boiroux
  • , John Bagterp Jørgensen*
  • *Corresponding author for this work
  • Novo Nordisk A/S

Research output: Contribution to journalConference articleResearchpeer-review

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Abstract

Model-based design of experiment (MBDoE) provides a framework to collect informative data for system identification. However, a parametric and structural mismatch between the design model and the underlying physical system can lead to hazardous experiments in safety critical systems. In this work, we present a method to safely improve system identification based on insights from a model-based optimal experimental design. From a visual inspection of a MBDoE, we select an approximated output curve fulfilling system constraints as a reference for the physical system. To avoid open-loop implementation of the MBDoE, we use our approximated reference together with a reference-tracking controller to collect experimental data in closed-loop. In this type 2 diabetes (T2D) case study, the proposed design method is safe and provides informative experimental data for system identification.

Original languageEnglish
Book seriesIFAC-PapersOnLine
Volume56
Issue number2
Pages (from-to)9654-9659
DOIs
Publication statusPublished - 2023
Event22nd IFAC World Congress
- Pacific Convention Plaza Yokohama, Yokohama, Japan
Duration: 9 Jul 202314 Jul 2023
Conference number: 22

Conference

Conference22nd IFAC World Congress
Number22
LocationPacific Convention Plaza Yokohama
Country/TerritoryJapan
CityYokohama
Period09/07/202314/07/2023
SponsorAzbil Corporation, Fujita Corporation, Hitachi, Ltd., Kumagai Gumi Co., Ltd., The Society of Instrument and Control Engineers (SICE)

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

Keywords

  • Artificial Pancreas
  • Diabetes
  • Insulin
  • Optimal Experimental Design
  • Simulation
  • System Identification

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