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

A model-based predictive control algorithm is developed to maintain normogl ycemia in the Type I diabetic patient using a closed-loop insulin infusion pump, Utilizing compartmental modeling techniques, a fundamental model of t he diabetic patient is constructed. The resulting nineteenth-order nonlinea r pharmacokinetic-pharmacodynamic representation is used in controller synt hesis. Linear identification of an input-output model from noisy patient da ta is performed by filtering the impulse-response coefficients via projecti on onto the Laguerre basis. A linear model predictive controller is develop ed using the identified step response model. Controller performance for unm easured disturbance rejection (50 g oral glucose tolerance test) is examine d. 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 st ate-estimating controller maintains glucose within 15 mg/dl of the setpoint in the presence of measurement noise, Under noise-free conditions, the mod el based predictive controller using state estimation outperforms an intern al 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 central in an insulin infusion p ump.