A model-based algorithm for blood glucose control in Type I diabetic patients

TLDR

The study develops a model‑based predictive control algorithm to maintain normoglycemia in Type I diabetic patients via a closed‑loop insulin infusion pump. The algorithm employs a compartmental 19th‑order nonlinear PKPD model, linearizes it via Laguerre‑basis identification, and implements a linear model‑predictive controller for closed‑loop insulin delivery. The state‑estimation controller keeps glucose within 15 mg/dl of the setpoint, achieving a 49.4 % undershoot reduction and 45.7 % faster settling time versus a literature internal‑model controller, showing promise for insulin‑pump glucose control.

Abstract

A model-based-predictive control algorithm is developed to maintain normoglycemia in the Type I diabetic patient using a closed-loop insulin infusion pump. Utilizing compartmental modeling techniques, a fundamental model of the diabetic patient is constructed. The resulting nineteenth-order nonlinear pharmacokinetic-pharmacodynamic representation is used in controller synthesis. Linear identification of an input-output model from noisy patient data is performed by filtering the impulse-response coefficients via projection onto the Laguerre basis. A linear model predictive controller is developed using the identified step response model. Controller performance for unmeasured disturbance rejection (50 g oral glucose tolerance test) is examined. Glucose setpoint tracking performance is improved by designing a second controller which substitutes a more detailed internal model including state-estimation and a Kalman filter for the input-output representation The state-estimating controller maintains glucose within 15 mg/dl of the setpoint in the presence of measurement noise. Under noise-free conditions, the model based predictive controller using state estimation outperforms an internal model controller from literature (49.4% reduction in undershoot and 45.7% reduction in settling time). These results demonstrate the potential use of predictive algorithms for blood glucose control in an insulin infusion pump.