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Adaptive control of artificial pancreas systems for treatment of type 1 diabetes 73
AL-MPC computation repeated at subsequent sampling instances using new
CGM measurements, updated states, and newly calculated penalty weights
of the objective function.
4Results
The efficacy of the proposed PMM-AP is demonstrated by using a multi-
variable simulator (mGIPsim) developed by our research group at Illinois
Institute of Technology based on a modified Hovorka’s glucose-insulin
dynamic model that takes into account the effects of different physical activ-
ities and meals (Rashid et al., 2019). In addition to the CGM values, the
mGIPsim generates physiological variable signals reported by noninvasive
wearable devices. MET is a physiological measure expressing the energy cost
of PA. MET is used to express the intensity and energy expenditure of PAs.
Aerobic exercises with a stationary bicycle are considered for testing the
PMM-AP system. Twenty virtual subjects are simulated for 30 days with
varying times and quantities of meals consumed on each day and different
intensities and times of physical activities as detailed in Tables 1 and 2.
The meal and PA information are not entered manually to the PMM-AP
system as the PMM-AP controller is designed to regulate the BGC in the
presence of significant disturbances such as unannounced meals and exer-
cises. The energy expenditure values expressed as MET variations are com-
puted by the simulator and used as input variables summarizing the
Table 1 Meal scenario for 30-day closed-loop experiment using mGIPsim.
Range for values
Meal
Time Amount (g)
Breakfast ½ 06 : 00,07 : 00 ½ 40,60
Lunch ½ 12 : 00,13 : 00 ½ 40,60
Dinner ½ 18 : 00,19 : 00 ½ 40,60
Table 2 Exercise scenario for 30-day closed-loop experiment using mGIPsim.
Range for values
Exercise
Time Duration (min) Power
Bicycling ½ 10 : 00,11 : 00 ½ 30,60 ½ 50,90
Bicycling ½ 16 : 00,17 : 00 ½ 30,60 ½ 50,90
