DUGi

Postprandial blood glucose control using a hybrid adaptive PD controller with insulin-on-board limitation

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This paper addresses the design of blood glucose control during the postprandial period for Type 1 diabetes patients. An artificial pancreas for ambulatory purposes has to deal with the delays inherent to the subcutaneous route, the carbohydrate intakes, the metabolic changes, the glucose sensor errors and noise, and the insulin pump constraints. A time response typically obtained in closed-loop insulin delivery shows hyperglycemia in the early postprandial period caused by the lag in the insulin absorbtion, followed by hypoglycemia caused by control over-reaction. A hybrid control system is proposed in this paper to overcome these problems. An insulin bolus is administered prior to the meals like in open-loop control, whereas a PD controller is used for robust glucose regulation. The controller gain is progressively increased after the bolus from zero up to its nominal value as function of the insulin on board, so that the PD controller becomes fully operational just when the insulin on board falls below a prescribed value. An excessive accumulation of active insulin is avoided in this way, drastically reducing the risk of hypoglycemia. The controller gain is adapted by means of a variable structure algorithm, allowing a very simple software implementation. The robust performance of the control algorithm is intensively assessed in silico on a cohort of virtual patients under challenging realistic scenarios considering mixed meals, circadian variations, time-varying uncertainties, discrete measurement and actuation, sensor errors and other disturbances

Research in this area is partially supported by the Spanish Ministry of Science and Innovation through grant DPI2010-20764-C02, the Government of Catalonia trough grant SGR00523, and the Argentinean Government (ANPCyT PICT-2011-0888, CONICET PIP112-2011-00361 and UNLP I164). Fabian León-Vargas acknowledges the FI grants of the Government of Catalonia

Elsevier