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.