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Iman Hajizadeh1, Mudassir Rashid1, Ali Cinar1,2

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This study introduces an adaptive artificial pancreas using model predictive control. It dynamically adjusts insulin delivery based on plasma insulin levels, improving safety and efficacy without meal input.

Keywords:
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Area of Science:

  • Biomedical Engineering
  • Control Systems Engineering
  • Computational Biology

Background:

  • Artificial pancreas (AP) systems aim to automate blood glucose management.
  • Current AP systems often require manual meal input, limiting their autonomy.
  • Accurate estimation of plasma insulin concentration (PIC) is crucial for effective insulin delivery.

Purpose of the Study:

  • To develop an adaptive model predictive control (MPC) algorithm for AP systems.
  • To enable autonomous insulin delivery without manual meal information.
  • To enhance AP safety and efficacy through dynamic constraint adjustments.

Main Methods:

  • Integrated a personalized compartment model for PIC estimation with recursive subspace-based system identification.
  • Developed an adaptive MPC algorithm using identified linear time-varying models.
  • Incorporated a dynamic safety constraint based on PIC estimates.

Main Results:

  • The system identification successfully characterized glycemic measurement dynamics.
  • The adaptive MPC algorithm computed optimal insulin delivery autonomously.
  • Simulations demonstrated improved AP efficacy and prevention of insulin overdosing.

Conclusions:

  • The proposed adaptive MPC algorithm offers a promising approach for autonomous AP systems.
  • Dynamic adjustment of control parameters based on PIC improves AP performance.
  • This method enhances safety and reduces the burden on patients by eliminating meal input requirements.