This paper addresses the problem of glucose control in the presence of sampled measurements. This topic is important in the study and development of the Artificial Pancreas (AP), which is a general expression to describe a set of techniques for the control of the glucose behaviour by means of exogenous insulin administration in diabetic individuals. Differently from most of the approaches available in the literature, we not only assume the lack of insulin measurements, but also the availability of the glucose measurements just at sampling times. An observer is designed for the model-based reconstruction of glucose and insulin trajectories from the glucose samples. On top of that, a feedback algorithm (based on the estimated state) is designed to continuously deliver exogenous intra-venous insulin to the patient. Simulations have been performed in-silico on models of virtual patients identified from real data and in the presence of quantization errors. The preliminary results highlight the potential of the proposed approach.

Glucose control with incomplete information

BORRI, ALESSANDRO;MANES, COSTANZO;PALUMBO, PASQUALE
2016

Abstract

This paper addresses the problem of glucose control in the presence of sampled measurements. This topic is important in the study and development of the Artificial Pancreas (AP), which is a general expression to describe a set of techniques for the control of the glucose behaviour by means of exogenous insulin administration in diabetic individuals. Differently from most of the approaches available in the literature, we not only assume the lack of insulin measurements, but also the availability of the glucose measurements just at sampling times. An observer is designed for the model-based reconstruction of glucose and insulin trajectories from the glucose samples. On top of that, a feedback algorithm (based on the estimated state) is designed to continuously deliver exogenous intra-venous insulin to the patient. Simulations have been performed in-silico on models of virtual patients identified from real data and in the presence of quantization errors. The preliminary results highlight the potential of the proposed approach.
9781509018970
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11697/111756
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