title: Enhanced Model Predictive Control (eMPC) Strategy for Automated Glucose Control.
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 bothin silicotests 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 throughin silicotrials based on a new clinical protocol, which incorporates induced hyper- and hypoglycemia to test robustness. The advancements are also evaluated in anadvisory 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.
authors.affiliation: Department of Chemical Engineering, University of California, Santa Barbara, CA 93106, USA; Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, 29 Oxford Street, Cambridge, MA 02138, USA; William Sansum Diabetes Center, 2219 Bath Street, Santa Barbara, CA 93105.
authors.forename: Joon Bok
authors.initials: JB
authors.lastname: Lee
authors.affiliation: Department of Chemical Engineering, University of California, Santa Barbara, CA 93106, USA; Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, 29 Oxford Street, Cambridge, MA 02138, USA; William Sansum Diabetes Center, 2219 Bath Street, Santa Barbara, CA 93105.
authors.forename: Eyal
authors.initials: E
authors.lastname: Dassau
authors.affiliation: Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, 29 Oxford Street, Cambridge, MA 02138, USA; William Sansum Diabetes Center, 2219 Bath Street, Santa Barbara, CA 93105.
authors.forename: Ravi
authors.initials: R
authors.lastname: Gondhalekar
authors.affiliation: Department of Chemical Engineering, University of California, Santa Barbara, CA 93106, USA.
authors.forename: Dale E
authors.initials: DE
authors.lastname: Seborg
authors.affiliation: William Sansum Diabetes Center, 2219 Bath Street, Santa Barbara, CA 93105.
authors.forename: Jordan E
authors.initials: JE
authors.lastname: Pinsker
authors.affiliation: Department of Chemical Engineering, University of California, Santa Barbara, CA 93106, USA; Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, 29 Oxford Street, Cambridge, MA 02138, USA; William Sansum Diabetes Center, 2219 Bath Street, Santa Barbara, CA 93105.
authors.forename: Francis J
authors.initials: FJ
authors.lastname: Doyle