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Lee JB, Dassau E, Gondhalekar R, et al. Enhanced Model Predictive Control (eMPC) Strategy for Automated Glucose Control. Ind Eng Chem Res. 2016;55(46):11857-11868doi: 10.1021/acs.iecr.6b02718.
Lee, J. B., Dassau, E., Gondhalekar, R., Seborg, D. E., Pinsker, J. E., & Doyle, F. J. (2016). Enhanced Model Predictive Control (eMPC) Strategy for Automated Glucose Control. Industrial & engineering chemistry research, 55(46), 11857-11868. https://doi.org/10.1021/acs.iecr.6b02718
Lee, Joon Bok, et al. "Enhanced Model Predictive Control (eMPC) Strategy for Automated Glucose Control." Industrial & engineering chemistry research vol. 55,46 (2016): 11857-11868. doi: https://doi.org/10.1021/acs.iecr.6b02718
Lee JB, Dassau E, Gondhalekar R, Seborg DE, Pinsker JE, Doyle FJ. Enhanced Model Predictive Control (eMPC) Strategy for Automated Glucose Control. Ind Eng Chem Res. 2016 Nov 23;55(46):11857-11868. doi: 10.1021/acs.iecr.6b02718. Epub 2016 Oct 27. PMID: 27942106; PMCID: PMC5144164.
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Ind Eng Chem Res. 2016 Nov 23;55(46):11857-11868. doi: 10.1021/acs.iecr.6b02718. Epub 2016 Oct 27.

Enhanced Model Predictive Control (eMPC) Strategy for Automated Glucose Control.

Industrial & engineering chemistry research

Joon Bok Lee, Eyal Dassau, Ravi Gondhalekar, Dale E Seborg, Jordan E Pinsker, Francis J Doyle

Affiliations

  1. Department of Chemical Engineering, University of California, Santa Barbara, CA 93106, USA; Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, 29 Oxford Street, Cambridge, MA 02138, USA; William Sansum Diabetes Center, 2219 Bath Street, Santa Barbara, CA 93105.
  2. Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, 29 Oxford Street, Cambridge, MA 02138, USA; William Sansum Diabetes Center, 2219 Bath Street, Santa Barbara, CA 93105.
  3. Department of Chemical Engineering, University of California, Santa Barbara, CA 93106, USA.
  4. William Sansum Diabetes Center, 2219 Bath Street, Santa Barbara, CA 93105.

PMID: 27942106 PMCID: PMC5144164 DOI: 10.1021/acs.iecr.6b02718

Abstract

Development of an effective artificial pancreas (AP) controller to deliver insulin autonomously to people with type 1 diabetes mellitus is a difficult task. In this paper, three enhancements to a clinically validated AP model predictive controller (MPC) are proposed that address major challenges facing automated blood glucose control, and are then evaluated by both

Conflict of interest statement

The authors declare no competing financial interest.

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