Bihormonal model predictive control of blood glucose in people with type 1 diabetes

Vladimir Batora, Marian Tarnik, Jan Murgas, Signe Schmidt, Kirsten Nørgaard, Niels Kjølstad Poulsen, Henrik Madsen, John Bagterp Jørgensen

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

Abstract

In this paper we present a bihormonal control system that controls blood glucose in people with type 1 diabetes (T1D). We use insulin together with glucagon to mitigate the negative effects of hyper- and hypoglycemia. The system consists of a Kalman filter, a micro-bolus insulin and glucagon infusion MPC, a mealtime bolus calculator and a CGM providing feedback to the controller. The controller employs a patient data-based prediction model with ARMAX structure. We test the controller using a bihormonal model with time-varying parameters for 3 subjects and compare its performance to a system with an identical insulin MPC, but a glucagon PD controller. The key contribution of the bihormonal MPC is the efficiency of glucagon use. We consider scenarios where the meals are estimated correctly or overestimated and where the insulin sensitivity increases. Both solutions provide tight glucose control. According to the simulations, the bihormonal MPC requires on average 30% less glucagon than the system with a PD controller.
Original languageEnglish
Title of host publicationProceedings of the IEEE Conference on Control Applications (CCA), 2014
PublisherIEEE
Publication date2014
Pages1693-1698
ISBN (Print)978-1-4799-7409-2
DOIs
Publication statusPublished - 2014
Event2014 IEEE Multi-Conference on Systems and Control - Antibes Congress Center, Antibes, France
Duration: 8 Oct 201410 Oct 2014
http://www.msc2014.org/

Conference

Conference2014 IEEE Multi-Conference on Systems and Control
LocationAntibes Congress Center
CountryFrance
CityAntibes
Period08/10/201410/10/2014
OtherAlso include the IEEE International Symposium on Intelligent Control (ISIC) 2014
Internet address

Keywords

  • Robotics and Control Systems
  • Computational modeling
  • Insulin
  • Kalman filters
  • PD control
  • Predictive models
  • Sugar

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