Abstract
This contribution presents an individualized multistage model predictive control (MS-MPC) algorithm for blood glucose (BG) stabilization and improved postprandial BG control for people with type 1 diabetes (T1D) with consistent meal patterns. The multistage formulation utilizes different meal patterns as disturbance realizations entering the glucose-insulin system, then assesses the best possible control input among all of the probable scenarios. The disturbance realizations, in the form of glucose rate of appearance traces, are estimated by using meal records (time and carbohydrate amount) as the input into an individualized oral model. Meal signatures are then clustered with the k-medoids algorithm to obtain meal patterns. Two approaches, a hybrid closed-loop (HCL) and fully closed-loop (FCL) MS-MPC were tested and compared with their respective control treatments (hybrid and fully automated MPC, respectively) using the complete in silico adult cohort of the FDA-accepted UVA/Padova metabolic simulator. Results confirm an improvement in both postprandial and overall percent time in 70-180 mg/dL 85.2 ± 15.5 v. 89.6 ± 12.2 and 94.1 ± 6.3 v. 95.7 ±5.0, respectively, using the HCL approach, and 37.8 ± 15.7 v. 63.4± 16.6 and 65.8 ± 12.7 v. 82.2± 9.2, using the FCL approach.P
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