Identification of a nonlinear model for a glucoregulatory benchmark problem

Laurent Vanbeylen, Anne Van Mulders, Amjad Abu-Rmileh

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


Recently, a novel identification method for a nonlinear dynamic model, called nonlinear Linear Fractional Representation (NL-LFR) model, has been developed. The model, composed of a static nonlinearity (SNL) surrounded by linear dynamics, can account for both nonlinear feed-forward and nonlinear feed-back effects. Using two classical frequency response measurements, the SNL is automatically recovered in a user-friendly and efficient (non-iterative) way. In this contribution, the method is illustrated on a glucoregulatory benchmark dataset (insulin-glucose relationship of the human body). The research on insulin-glucose models is essential to develop methodologies to control the blood glucose level in diabetes patients. The obtained results outperform earlier results on the same benchmark data, while providing an excellent accuracy-complexity tradeoff.

Original languageEnglish
Pages (from-to)168-173
Number of pages6
JournalBiomedical Signal Processing and Control
Issue number1
StatePublished - 1 Jan 2014


  • Artificial pancreas
  • Best linear approximation
  • Black-box modelling
  • Insulin-glucose modelling
  • Nonlinear fractional representation
  • Nonlinear models
  • System identification

ASJC Scopus subject areas

  • Signal Processing
  • Health Informatics


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