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Neural predictive controller for insulin delivery using the subcutaneous route

Z Trajanoski1, P Wach

  • 1Department of Biophysics, Graz University of Technology, Austria. trajanoski@ibmt.tu-graz.ac.at

IEEE Transactions on Bio-Medical Engineering
|September 15, 1998
PubMed
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A novel neural predictive controller offers stable glucose regulation via subcutaneous insulin infusion, even with noise and time delays. Patient mealtime adjustments remain necessary for optimal control.

Area of Science:

  • Biomedical Engineering
  • Control Systems
  • Computational Neuroscience

Background:

  • Closed-loop glucose control is crucial for diabetes management.
  • Subcutaneous (s.c.) insulin delivery and glucose monitoring present unique challenges.
  • Existing control strategies often struggle with system variability and delays.

Purpose of the Study:

  • To develop and evaluate a neural predictive controller for closed-loop glucose regulation.
  • To utilize subcutaneous glucose measurements and insulin infusion for control.
  • To assess controller performance under various simulated conditions.

Main Methods:

  • Employed off-line system identification using neural networks (NNs).
  • Designed a nonlinear model predictive controller (NMPC).

Related Experiment Videos

  • Integrated nonlinear autoregressive with exogenous inputs (NARX) models and radial basis function NNs for system identification.

    • Main Results:

    • Achieved stable glucose control despite significant noise levels and time delays.
    • Demonstrated robustness to slow time variations in the glucose regulation process.
    • Identified limitations in s.c. insulin administration, necessitating patient intervention at mealtimes.

    Conclusions:

    • The developed neural predictive controller shows promise for automated glucose management.
    • The system identification framework effectively models complex glucose dynamics.
    • Further refinement is needed to fully address limitations of subcutaneous insulin delivery.