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Closed-loop control of artificial pancreatic Beta -cell in type 1 diabetes mellitus using model predictive iterative learning control.
IEEE Trans Biomed Eng. 2010 Feb; 57(2):211-9.IT

Abstract

A novel combination of iterative learning control (ILC) and model predictive control (MPC), referred to here as model predictive iterative learning control (MPILC), is proposed for glycemic control in type 1 diabetes mellitus. MPILC exploits two key factors: frequent glucose readings made possible by continuous glucose monitoring technology; and the repetitive nature of glucose-meal-insulin dynamics with a 24-h cycle. The proposed algorithm can learn from an individual's lifestyle, allowing the control performance to be improved from day to day. After less than 10 days, the blood glucose concentrations can be kept within a range of 90-170 mg/dL. Generally, control performance under MPILC is better than that under MPC. The proposed methodology is robust to random variations in meal timings within +/-60 min or meal amounts within +/-75% of the nominal value, which validates MPILC's superior robustness compared to run-to-run control. Moreover, to further improve the algorithm's robustness, an automatic scheme for setpoint update that ensures safe convergence is proposed. Furthermore, the proposed method does not require user intervention; hence, the algorithm should be of particular interest for glycemic control in children and adolescents.

Authors+Show Affiliations

Department of Chemical Engineering and Biomolecular Science and Engineering Program, University of California, Santa Barbara, CA 93106, USA. kewangyq@engr.ucsb.eduNo affiliation info availableNo affiliation info available

Pub Type(s)

Journal Article
Research Support, Non-U.S. Gov't

Language

eng

PubMed ID

19527957

Citation

Wang, Youqing, et al. "Closed-loop Control of Artificial Pancreatic Beta -cell in Type 1 Diabetes Mellitus Using Model Predictive Iterative Learning Control." IEEE Transactions On Bio-medical Engineering, vol. 57, no. 2, 2010, pp. 211-9.
Wang Y, Dassau E, Doyle FJ. Closed-loop control of artificial pancreatic Beta -cell in type 1 diabetes mellitus using model predictive iterative learning control. IEEE Trans Biomed Eng. 2010;57(2):211-9.
Wang, Y., Dassau, E., & Doyle, F. J. (2010). Closed-loop control of artificial pancreatic Beta -cell in type 1 diabetes mellitus using model predictive iterative learning control. IEEE Transactions On Bio-medical Engineering, 57(2), 211-9. https://doi.org/10.1109/TBME.2009.2024409
Wang Y, Dassau E, Doyle FJ. Closed-loop Control of Artificial Pancreatic Beta -cell in Type 1 Diabetes Mellitus Using Model Predictive Iterative Learning Control. IEEE Trans Biomed Eng. 2010;57(2):211-9. PubMed PMID: 19527957.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Closed-loop control of artificial pancreatic Beta -cell in type 1 diabetes mellitus using model predictive iterative learning control. AU - Wang,Youqing, AU - Dassau,Eyal, AU - Doyle,Francis J,3rd Y1 - 2009/06/12/ PY - 2009/6/17/entrez PY - 2009/6/17/pubmed PY - 2010/4/3/medline SP - 211 EP - 9 JF - IEEE transactions on bio-medical engineering JO - IEEE Trans Biomed Eng VL - 57 IS - 2 N2 - A novel combination of iterative learning control (ILC) and model predictive control (MPC), referred to here as model predictive iterative learning control (MPILC), is proposed for glycemic control in type 1 diabetes mellitus. MPILC exploits two key factors: frequent glucose readings made possible by continuous glucose monitoring technology; and the repetitive nature of glucose-meal-insulin dynamics with a 24-h cycle. The proposed algorithm can learn from an individual's lifestyle, allowing the control performance to be improved from day to day. After less than 10 days, the blood glucose concentrations can be kept within a range of 90-170 mg/dL. Generally, control performance under MPILC is better than that under MPC. The proposed methodology is robust to random variations in meal timings within +/-60 min or meal amounts within +/-75% of the nominal value, which validates MPILC's superior robustness compared to run-to-run control. Moreover, to further improve the algorithm's robustness, an automatic scheme for setpoint update that ensures safe convergence is proposed. Furthermore, the proposed method does not require user intervention; hence, the algorithm should be of particular interest for glycemic control in children and adolescents. SN - 1558-2531 UR - https://www.unboundmedicine.com/medline/citation/19527957/Closed_loop_control_of_artificial_pancreatic_Beta__cell_in_type_1_diabetes_mellitus_using_model_predictive_iterative_learning_control_ L2 - https://dx.doi.org/10.1109/TBME.2009.2024409 DB - PRIME DP - Unbound Medicine ER -