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- W2886839763 abstract "An adaptive zone model predictive control design problem is considered for enhanced blood glucose regulation in patients with type 1 diabetes mellitus. The key contribution of this work is the development of a zone MPC with a dynamic cost function that updates its control penalty parameters based on the predicted glucose and its rate of change. A parameter adaptation law is proposed by explicitly constructing maps from glucose state and velocity spaces to control penalty parameter spaces. The proposed controller is tested on the to-patient cohort of the US Food and Drug Administration accepted Universities of Virginia/Padova simulator and compared with the zone model predictive control without parameter adaptation. The obtained in-silico results indicate that for unannounced meals, the controller leads to statistically significant improvements in terms of mean glucose level (154.2 mg/dL vs. 160.7 mg/dL; and percentage time in the safe euglycemic range of [70, 180] mg/dL (72.7% vs. 67.5%; ) without increasing the risk of hypoglycemia (percentage time below 70 mg/dL, 0.0% vs. 0.0%; p=0.788). For announced meals, the obtained performance is similar (and slightly superior) to that of the zone model predictive control without adaptation in terms of mean glucose level (135.6 mg/dL vs. 136.5 mg/dL; ), percentage time in [70, 180] mg/dL (91.2% vs. 90.9%; p=0.04), and percentage time below 70 mg/dL (0.0% vs. 0.0%; p=0.346)." @default.
- W2886839763 created "2018-08-22" @default.
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- W2886839763 date "2018-06-01" @default.
- W2886839763 modified "2023-10-18" @default.
- W2886839763 title "Zone Model Predictive Control with Glucose- and Velocity-Dependent Control Penalty Adaptation for an Artificial Pancreas" @default.
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- W2886839763 doi "https://doi.org/10.23919/acc.2018.8431902" @default.
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