Multiple-Input Subject-Specific Modeling of Plasma Glucose Concentration for Feedforward Control

• Published in: Industrial & engineering chemistry research Vol. 53; no. 47; pp. 18216 - 18225
• Main Authors: Kotz, Kaylee, Cinar, Ali, Mei, Yong, Roggendorf, Amy, Littlejohn, Elizabeth, Quinn, Laurie, Rollins, Derrick K
• Format: Journal Article
• Language: English
• Published: United States American Chemical Society 26.11.2014
• Subjects: Automatic control systems; automation; blood glucose; Causation; chemistry; Consumption; diabetes; Disturbances; Feedforward control; Glucose; Insulin; Pancreas
• ISSN: 0888-5885; 1520-5045; 1520-5045
• DOI: 10.1021/ie404119b

The ability to accurately develop subject-specific, input causation models, for blood glucose concentration (BGC) for large input sets can have a significant impact on tightening control for insulin dependent diabetes. More specifically, for Type 1 diabetics (T1Ds), it can lead to an effective artificial pancreas (i.e., an automatic control system that delivers exogenous insulin) under extreme changes in critical disturbances. These disturbances include food consumption, activity variations, and physiological stress changes. Thus, this paper presents a free-living, outpatient, multiple-input, modeling method for BGC with strong causation attributes that is stable and guards against overfitting to provide an effective modeling approach for feedforward control (FFC). This approach is a Wiener block-oriented methodology, which has unique attributes for meeting critical requirements for effective, long-term, FFC.