Performance Analysis of Different Embedded Systems and Open-Source Optimization Packages Towards an Impulsive MPC Artificial Pancreas

Mora, Jhon Edison Goez; Villa-Tamayo, María F.; Vallejo, Monica; Rivadeneira, Pablo S.

doi:10.3389/fendo.2021.662348

Cited by 4 publications

(1 citation statement)

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“…To solve the optimization problem at each time step, the quadprog solver of Matlab was used with the interior-point-convex algorithm and with tolerances TolPGG= 1×10 -5 , Tolcon=1×10 -4 , TolX= 1×10 -4 , and Tolfun=1 × 10 -4 (see ( 43 ) for more details on the selection of the solver for quadratic programming in artificial pancreas context).…”

Section: Methodsmentioning

confidence: 99%

Interval Safety Layer Coupled With an Impulsive MPC for Artificial Pancreas to Handle Intrapatient Variability

et al. 2022

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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.

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Section: Methodsmentioning

confidence: 99%