Page 82 - Control Theory in Biomedical Engineering

Adaptive control of artificial pancreas systems for treatment of type 1 diabetes 69

Fig. 3 A flowchart of the proposed recursive system identification technique.

information, estimates of the meal effect, and physiological variables rep-
resent inputs to indicate physical activity (PA) (u k ¼ [Ins k , Meal k , MET k ]).
The metabolic equivalent task (MET) is the metabolic equivalent of task
that represents energy expenditure, as an indicator of the intensity of PA.
One of the states of the model described by Eq. (2) is the amount of insulin
in the bloodstream, the PIC. The PIC safety constraints are then defined in
the AL-MPC to assure that a safe amount of insulin is in the body.
A general flowchart of the proposed identification technique is shown
in Fig. 3. To guarantee that the underlying model can provide accurate
output predictions for use in model-based predictive control algorithms,
the proposed recursive subspace identification method obtains a stable
time-varying state-space model of the process. This is done by incorpora-
tion of constraints on the fidelity and accuracy of the identified models, the
correctness of the sign of the input-to-output gains, and the integration of
heuristics to ensure the stability of the recursively identified models
(Hajizadeh et al., 2018a).

3 PIC cognizant AL-MPC algorithm
In this section, we describe the glycemic and PIC risk indexes used in the
AL-MPC controller objective function. The safety constraints based on
the PIC and a feature extraction method for manipulating these constraints