Closed-loop control with unannounced exercise for adults with type 1 diabetes using the Ensemble Model Predictive Control

Background: Recent progress in the development of clinically accurate continuous glucose monitors (CGMs), automated continuous insulin infusion pumps, and control algorithms for calculating insulin doses from CGM data have enabled the development of prototypes of subcutaneous closed-loop systems for controlling blood glucose (BG) levels in type 1 diabetes. The use of a new personalized model predictive control (MPC) algorithm to determine insulin doses to achieve and maintain BG levels between 70 and 140 mg/dl overnight and to control postprandial BG levels is presented. Methods: Eight adults with type 1 diabetes were studied twice, once using their personal open-loop systems to control BG overnight and for 4 h following a standardized meal and once using a closed-loop system that utilizes the MPC algorithm to control BG overnight and for 4 h following a standardized meal. Average BG levels, percentage of time within BG target of 70–140 mg/dl, number of hypoglycemia episodes, and postprandial BG excursions during both study periods were compared. Results: With closed-loop control, once BG levels achieved the target range (70–140 mg/dl), they remained within that range throughout the night in seven of the eight subjects. One subject developed a BG level of 65 mg/dl, which was signaled by the CGM trend analysis, and the MPC algorithm directed the discontinuance of the insulin infusion. The number of overnight hypoglycemic events was significantly reduced ( p = .011) with closed-loop control. Postprandial BG excursions were similar during closed-loop and open-loop control Conclusion: Model predictive closed-loop control of BG levels can be achieved overnight and following a standardized breakfast meal. This “artificial pancreas” controls BG levels as effectively as patient-directed open-loop control following a morning meal but is significantly superior to open-loop control in preventing overnight hypoglycemia.

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Insulin Dosage Optimization Based on Model Predictive Control with the Model Parameters Estimation

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.971-973.1222 ◽

2014 ◽

Vol 971-973 ◽

pp. 1222-1229

Author(s):

Sha Sha Zhang ◽

Ming Zhong ◽

Ya Xin Liu

Keyword(s):

Type 1 Diabetes ◽

Predictive Control ◽

Glucose Concentration ◽

Closed Loop ◽

Control Method ◽

Parameters Estimation ◽

Model Parameters ◽

Closed Loop Control ◽

Loop Control

Diabetes is a disease characterized by the inability of the pancreas to regulate blood glucose concentration. The development of the closed-loop control method for the treatment of type 1 diabetes is a highly desired endeavor for patients, physicians and scientists. Focusing on the need for maintaining normal glycaemia, the paper proposes an integrated closed-loop control method, which is the model predictive control (MPC) method combined with model parameters estimation. Using the static glucose model to be the internal model can’t achieve the good performance in different objects because of the individual differences. By updating the internal controller model with current measurement information using the parameters estimation, mismatch between the actual controlled object and the internal controller model is significantly reduced. Therefore, the predictions of future glucose values using the updated model are more accurate than those of the static input–output model. Finally, the integrated control method was tested on the T1DM simulator which was accepted by the FDA. Four objects are selected randomly in T1DM simulator. The control results showed it can modulate the glucose concentration to 85~120 mg/dl without severe hypoglycemia in the effective time. In conclusion, it is promising for the control of glucose concentration in subjects with type 1 diabetes.

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