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Controlling the AP Controller: Controller Performance Assessment and Modification

Overview
Specialty Endocrinology
Date 2019 Sep 29
PMID 31561714
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Abstract

Background: Despite recent advances in closed-loop control of blood glucose concentration (BGC) in people with type 1 diabetes (T1D), online performance assessment and modification of artificial pancreas (AP) control systems remain a challenge as the metabolic characteristics of users change over time.

Methods: A controller performance assessment and modification system (CPAMS) analyzes the glucose concentration variations and controller behavior, and modifies the parameters of the control system used in the multivariable AP system. Various indices are defined to quantitatively evaluate the controller performance in real time. Controller performance assessment and modification system also incorporates online learning from historical data to anticipate impending disturbances and proactively counteract their effects.

Results: Using a multivariable simulation platform for T1D, the CPAMS is used to enhance the BGC regulation in people with T1D by means of automated insulin delivery with an adaptive learning predictive controller. Controller performance assessment and modification system increases the percentage of time in the target range (70-180) mg/dL by 52.3% without causing any hypoglycemia and hyperglycemia events.

Conclusions: The results demonstrate a significant improvement in the multivariable AP controller performance by using CPAMS.

References
1.
Breton M, Farret A, Bruttomesso D, Anderson S, Magni L, Patek S . Fully integrated artificial pancreas in type 1 diabetes: modular closed-loop glucose control maintains near normoglycemia. Diabetes. 2012; 61(9):2230-7. PMC: 3425406. DOI: 10.2337/db11-1445. View

2.
Ly T, Weinzimer S, Maahs D, Sherr J, Roy A, Grosman B . Automated hybrid closed-loop control with a proportional-integral-derivative based system in adolescents and adults with type 1 diabetes: individualizing settings for optimal performance. Pediatr Diabetes. 2016; 18(5):348-355. DOI: 10.1111/pedi.12399. View

3.
Campos-Cornejo F, Campos-Delgado D, Espinoza-Trejo D, Zisser H, Jovanovic L, Doyle 3rd F . An advisory protocol for rapid- and slow-acting insulin therapy based on a run-to-run methodology. Diabetes Technol Ther. 2010; 12(7):555-65. DOI: 10.1089/dia.2009.0173. View

4.
Forlenza G, Deshpande S, Ly T, Howsmon D, Cameron F, Baysal N . Application of Zone Model Predictive Control Artificial Pancreas During Extended Use of Infusion Set and Sensor: A Randomized Crossover-Controlled Home-Use Trial. Diabetes Care. 2017; 40(8):1096-1102. PMC: 5521973. DOI: 10.2337/dc17-0500. View

5.
Hajizadeh I, Rashid M, Cinar A . Plasma-Insulin-Cognizant Adaptive Model Predictive Control for Artificial Pancreas Systems. J Process Control. 2019; 77:97-113. PMC: 6897508. DOI: 10.1016/j.jprocont.2019.03.009. View