A run-to-run control strategy to adjust basal insulin infusion rates in type 1 diabetes

Abstract

Maintaining good glycemic control is a daily challenge for people with type 1 diabetes. Insulin requirements are changing constantly due to many factors, such as levels of stress and physical activity. The basal insulin requirement also has a circadian rhythm, adding another level of complexity. Automating the adjustment of insulin dosing would result in improved glycemic control, as well as an improved quality of life by significantly reducing the burden on the patient. Building on our previous success of using run-to-run control for prandial insulin dosing (a strategy adapted from the chemical process industry), we show how this same framework can be used to adjust basal infusion profiles. We present a mathematical model of insulin-glucose dynamics which we augment in order to capture the circadian variation in insulin requirements. Using this model, we show that the run-to-run framework can also be successfully applied to adjust basal insulin dosing.

A robust sliding mode controller with internal model for closed-loop artificial pancreas

“…Postprandial performance was superior in IMC-SMC with feedforward, highlighting the limitations of purely reactive control algorithms. The smooth curve used to approximate the variations in insulin sensitivity provides a more realistic approximation for the gradual change in insulin sensitivity during the day than constant or stepchanging sensitivity used in most of previous works (see 10 for example [2,11,22,24,25]). The designed controller was able to handle up to ±50% error in meal announcement-which is a gross error in comparison with errors usually used in literature [22,23], without reaching the severe limits of 50 and 280 mg/dl.…”

Run-to-Run Tuning of Model Predictive Control for Type 1 Diabetes Subjects: In Silico Trial

“…During experiments 2-3 (days [1][2][3][4][5][6][7][8][9][10][11][12][13][14], the run-to-run procedure attains a good glycemic regulation within a few days. The subsequent meal perturbations during experiment 4 (days 15-24) produce some excursions that are, however, kept under control.…”

Controlling blood glucose variability under uncertainty using reinforcement learning and Gaussian processes

“…These hybrid methods try to benefit from the repetitive nature of insulin therapy to improve iteratively the efficacy of insulin doses by using run-to-run control algorithms [20,21,42,43]. In a situation with frequent data sampling, iterative learning control (ILC) is the alternative of choice.…”