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Comput Chem Eng. 2018 Apr 6;112:57-69. doi: 10.1016/j.compchemeng.2018.02.002. Epub 2018 Feb 10.

Multi-level Supervision and Modification of Artificial Pancreas Control System.

Author information

1
Department of Chemical and Biological Engineering, Illinois Institute of Technology, Chicago, IL, USA.
2
Department of Control Theory and Control Engineering, Northeastern University, Shenyang, Liaoning China.
3
Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, IL, USA.
4
Department of Electrical and Computer Engineering, Illinois Institute of Technology, Chicago, IL, USA.
5
Department of Pediatrics, University of Chicago, Chicago, IL, USA.
6
Departments of Medicine and Pediatrics - Section of Endocrinology, University of Chicago, Chicago, IL, USA.

Abstract

Artificial pancreas (AP) systems provide automated regulation of blood glucose concentration (BGC) for people with type 1 diabetes (T1D). An AP includes three components: a continuous glucose monitoring (CGM) sensor, a controller calculating insulin infusion rate based on the CGM signal, and a pump delivering the insulin amount calculated by the controller to the patient. The performance of the AP system depends on successful operation of these three components. Many APs use model predictive controllers that rely on models to predict BGC and to calculate the optimal insulin infusion rate. The performance of model-based controllers depends on the accuracy of the models that is affected by large dynamic changes in glucose-insulin metabolism or equipment performance that may move the operating conditions away from those used in developing the models and designing the control system. Sensor errors and missing signals will cause calculation of erroneous insulin infusion rates. And the performance of the controller may vary at each sampling step and each period (meal, exercise, and sleep), and from day to day. Here we describe a multi-level supervision and controller modification (ML-SCM) module is developed to supervise the performance of the AP system and retune the controller. It supervises AP performance in 3 time windows: sample level, period level, and day level. At sample level, an online controller performance assessment sub-module will generate controller performance indexes to evaluate various components of the AP system and conservatively modify the controller. A sensor error detection and signal reconciliation module will detect sensor error and reconcile the CGM sensor signal at each sample. At period level, the controller performance is evaluated with information collected during a certain time period and the controller is tuned more aggressively. At the day level, the daily CGM ranges are further analyzed to determine the adjustable range of controller parameters used for sample level and period level. Thirty subjects in the UVa/Padova metabolic simulator were used to evaluate the performance of the ML-SCM module and one clinical experiment is used to illustrate its performance in a clinical environment. The results indicate that the AP system with an ML-SCM module has a safer range of glucose concentration distribution and more appropriate insulin infusion rate suggestions than an AP system without the ML-SCM module.

KEYWORDS:

Artificial Pancreas; Controller Performance Assessment; Controller Retuning; Sensor Error Detection; Type 1 Diabetes

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