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CHAPTER 3

Adaptive control of artificial

pancreas systems for treatment

of type 1 diabetes

a
b
a
Iman Hajizadeh , Mohammad Reza Askari , Mert Sevil ,
b
a
b
Nicole Hobbs , Rachel Brandt , Mudassir Rashid , Ali Cinar a,b
a
Department of Chemical and Biological Engineering, Illinois Institute of Technology, Chicago, IL,
United States
b
Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, IL, United States
1 Introduction
The applications of systems engineering in the area of health care are numer-
ous (Ogunnaike, 2019; Doyle et al., 2007; Goodwin et al., 2019; Parker,
2009; Parker and Doyle, 2001). They include automated insulin delivery
systems for people with type 1 diabetes (T1D) (Hajizadeh et al., 2019c;
Garcia-Tirado et al., 2019; Hovorka et al., 2004), modeling and control
for cancer treatment (Martin, 1992; Martin and Teo, 1994), drug infusion
in critical care (Yu et al., 1992; Behbehani and Cross, 1991), and developing
optimal treatment for HIV infection (Hajizadeh and Shahrokhi, 2015;
Pannocchia et al., 2010).
One of the most pressing medical problems in the world today is the
growing epidemic of diabetes. People with T1D have β cells in the pancreas
that do not produce any insulin. Consequently, people with T1D are unable
to regulate their blood glucose concentration (BGC) without injecting
appropriate amounts of insulin through either multiple daily injections or
continuous subcutaneous insulin infusion (CSII) (Eisenbarth, 2005; Cooke
and Plotnick, 2008).
Glycemic control can be enhanced in people with T1D using multivar-
iable artificial pancreas (AP) systems that automatically compute the required
amount of insulin (Turksoy et al., 2018; Hajizadeh et al., 2019c). This tech-
nology integrates a continuous glucose monitoring (CGM) sensor, a wear-
able device, a CSII pump, and control algorithms. Real-time measurements
for feedback/feedforward control, accurate estimates of nonmeasurable
physiological variables of patients, an accurate dynamic model of the human

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