Abstract
The aim of control strategies for artificial pancreas systems is to calculate the insulin doses required by a subject with type 1 diabetes to regulate blood glucose levels by reducing hyperglycemia and avoiding the induction of hypoglycemia. Several control formulations developed for this end involve a safety constraint given by the insulin on board (IOB) estimation. This constraint has the purpose of reducing hypoglycemic episodes caused by insulin stacking. However, intrapatient variability constantly changes the patient’s response to insulin, and thus, an adaptive method is required to restrict the control action according to the current situation of the subject. In this work, the control action computed by an impulsive model predictive controller is modulated with a safety layer to satisfy an adaptive IOB constraint. This constraint is established with two main steps. First, upper and lower IOB bounds are generated with an interval model that accounts for parameter uncertainty, and thus, define the possible system responses. Second, the constraint is selected according to the current value of glycemia, an estimation of the plant-model mismatch, and their corresponding first and second time derivatives to anticipate the changes of both glucose levels and physiological variations. With this strategy satisfactory results were obtained in an adult cohort where random circadian variability and sensor noise were considered. A 92% time in normoglycemia was obtained, representing an increase of time in range compared to previous MPC strategies, and a reduction of time in hypoglycemia to 0% was achieved without dangerously increasing the time in hyperglycemia.
Highlights
Managing type 1 diabetes (T1D) has proven to be challenging
This paper addresses the limitation found in previous works to obtain a control strategy that includes an adaptive safety module
To evaluate the control strategy, 10 virtual adult subjects were simulated by identifying the model parameters from the commercially available version of the UVA-Padova T1DM Simulator [36]
Summary
Managing type 1 diabetes (T1D) has proven to be challenging. People with T1D need exogenous insulin to regulate their blood glucose (BG) levels. The therapy required involves a risk of severe hypoglycemia, with all its consequences, if the insulin dose is too high. For this reason, the therapeutic goal is to minimize the number of hypoglycemic episodes and maximize time in the Interval-Layer MPC for Artificial Pancreas healthy glycemic range, known as normoglycemia zone. There are several studies evaluating AP performance using different control strategies that have shown efficiency in clinical and free-living-condition trials [5,6,7]. AP systems with dedicated safety schemes and/or adaptive laws are preferred over traditional control systems to reduce the risk of hypoglycemia in both fully closed-loop or hybrid developments [8,9,10]
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