Blood glucose concentration control for type 1 diabetic patients: a non-linear suboptimal approach

Area of Science:

• Biomedical Engineering
• Control Systems Engineering
• Endocrinology

Background:

• Type 1 diabetes management requires precise glucose control to prevent complications.
• Existing control strategies face challenges with patient variability and external factors like meals and exercise.
• Accurate estimation of physiological states is crucial for effective closed-loop control.

Purpose of the Study:

• To propose and evaluate a closed-loop treatment strategy for blood glucose regulation in type 1 diabetes.
• To design a non-linear filter for estimating unmeasured state variables in the glucose-insulin system.
• To assess the robustness of the control strategy against parametric uncertainties and external disturbances.

Main Methods:

• Development of a closed-loop control system using the state-dependent Riccati equation (SDRE) tracker.
• Design of a non-linear filter to estimate unobserved states (e.g., insulin action) from blood glucose measurements.
• Simulation studies on a nominal patient and nine type 1 diabetic patients with varying parameters.
• Inclusion of unannounced meals and regular exercise scenarios in simulations.

Main Results:

• The proposed closed-loop strategy demonstrated effective blood glucose control in simulations.
• The non-linear filter successfully estimated the unmeasured state variables.
• The system showed robustness to parametric uncertainties and observation noise.
• Simulations confirmed the strategy's effectiveness under various conditions, including meal intake and exercise.

Conclusions:

• The developed closed-loop system offers a promising approach for automated blood glucose management in type 1 diabetes.
• The integration of SDRE control and a non-linear filter enhances system performance and robustness.
• This strategy holds potential for improving the quality of life for individuals with type 1 diabetes.