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Enhanced Model Predictive Control (eMPC) Strategy for Automated Glucose Control
- Source :
- Industrial & Engineering Chemistry Research. 55:11857-11868
- Publication Year :
- 2016
- Publisher :
- American Chemical Society (ACS), 2016.
-
Abstract
- Development of an effective artificial pancreas (AP) controller to deliver insulin autonomously to people with type 1 diabetes mellitus is a difficult task. In this paper, three enhancements to a clinically validated AP model predictive controller (MPC) are proposed that address major challenges facing automated blood glucose control, and are then evaluated by both in silico tests and clinical trials. First, the core model of insulin-blood glucose dynamics utilized in the MPC is expanded with a medically inspired personalization scheme to improve controller responses in the face of inter- and intra-individual variations in insulin sensitivity. Next, the asymmetric nature of the short-term consequences of hypoglycemia versus hyperglycemia is incorporated in an asymmetric weighting of the MPC cost function. Finally, an enhanced dynamic insulin-on-board algorithm is proposed to minimize the likelihood of controller-induced hypoglycemia following a rapid rise of blood glucose due to rescue carbohydrate load with accompanying insulin suspension. Each advancement is evaluated separately and in unison through in silico trials based on a new clinical protocol, which incorporates induced hyper- and hypoglycemia to test robustness. The advancements are also evaluated in an advisory mode (simulated) testing of clinical data. The combination of the three proposed advancements show statistically significantly improved performance over the nonpersonalized controller without any enhancements across all metrics, displaying increased time in the 70–180 mg/dL safe glycemic range (76.9 versus 68.8%) and the 80–140 mg/dL euglycemic range (48.1 versus 44.5%), without a statistically significant increase in instances of hypoglycemia. The proposed advancements provide safe control action for AP applications, personalizing and improving controller performance without the need for extensive model identification processes.
- Subjects :
- Type 1 diabetes
Glucose control
Computer science
General Chemical Engineering
Insulin
medicine.medical_treatment
Insulin sensitivity
030209 endocrinology & metabolism
General Chemistry
Hypoglycemia
medicine.disease
Artificial pancreas
Article
Industrial and Manufacturing Engineering
Weighting
03 medical and health sciences
Model predictive control
0302 clinical medicine
Control theory
medicine
030212 general & internal medicine
Subjects
Details
- ISSN :
- 15205045 and 08885885
- Volume :
- 55
- Database :
- OpenAIRE
- Journal :
- Industrial & Engineering Chemistry Research
- Accession number :
- edsair.doi.dedup.....68b8f7b3cfe82fcdb01f647ba716ee50