In this work, the problem of regulating blood glucose (glycemia) in type I diabetic patients is studied bymeans of an impulsive zonemodel predictive control (iZMPC), which bases its predictions on a novel long-term glucose-insulin model. Taking advantage of the impulsive version of the model—which features real-life properties of diabetes patients that some other popular models do not—the given control guarantees the stability under moderate-to-severe plant-model mismatch and disturbances. Long-term scenarios—including meals and physiological parameter variations—are simulated and the results are satisfactory as every hyperglycemic and hypoglycemic episodes are suitably controlled.

(2020). Stable impulsive zone model predictive control for type 1 diabetic patients based on a long-term model [journal article - articolo]. In OPTIMAL CONTROL APPLICATIONS & METHODS. Retrieved from http://hdl.handle.net/10446/169408

Stable impulsive zone model predictive control for type 1 diabetic patients based on a long-term model

Ferramosca, Antonio;
2020-01-01

Abstract

In this work, the problem of regulating blood glucose (glycemia) in type I diabetic patients is studied bymeans of an impulsive zonemodel predictive control (iZMPC), which bases its predictions on a novel long-term glucose-insulin model. Taking advantage of the impulsive version of the model—which features real-life properties of diabetes patients that some other popular models do not—the given control guarantees the stability under moderate-to-severe plant-model mismatch and disturbances. Long-term scenarios—including meals and physiological parameter variations—are simulated and the results are satisfactory as every hyperglycemic and hypoglycemic episodes are suitably controlled.
articolo
2020
González, Alejandro H.; Rivadeneira, Pablo S.; Ferramosca, Antonio; Magdelaine, Nicolas; Moog, Claude H.
(2020). Stable impulsive zone model predictive control for type 1 diabetic patients based on a long-term model [journal article - articolo]. In OPTIMAL CONTROL APPLICATIONS & METHODS. Retrieved from http://hdl.handle.net/10446/169408
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