2 research outputs found
Model Predictive Control (MPC) of an Artificial Pancreas with Data-Driven Learning of Multi-Step-Ahead Blood Glucose Predictors
We present the design and \textit{in-silico} evaluation of a closed-loop
insulin delivery algorithm to treat type 1 diabetes (T1D) consisting in a
data-driven multi-step-ahead blood glucose (BG) predictor integrated into a
Linear Time-Varying (LTV) Model Predictive Control (MPC) framework. Instead of
identifying an open-loop model of the glucoregulatory system from available
data, we propose to directly fit the entire BG prediction over a predefined
prediction horizon to be used in the MPC, as a nonlinear function of past
input-ouput data and an affine function of future insulin control inputs. For
the nonlinear part, a Long Short-Term Memory (LSTM) network is proposed, while
for the affine component a linear regression model is chosen. To assess
benefits and drawbacks when compared to a traditional linear MPC based on an
auto-regressive with exogenous (ARX) input model identified from data, we
evaluated the proposed LSTM-MPC controller in three simulation scenarios: a
nominal case with 3 meals per day, a random meal disturbances case where meals
were generated with a recently published meal generator, and a case with 25
decrease in the insulin sensitivity. Further, in all the scenarios, no
feedforward meal bolus was administered. For the more challenging random meal
generation scenario, the mean standard deviation percent time in the
range 70-180 [mg/dL] was 74.99 7.09 vs. 54.15 14.89, the mean
standard deviation percent time in the tighter range 70-140 [mg/dL] was
47.788.55 vs. 34.62 9.04, while the mean standard deviation
percent time in sever hypoglycemia, i.e., 54 [mg/dl] was 1.003.18 vs.
9.4511.71, for our proposed LSTM-MPC controller and the traditional
ARX-MPC, respectively. Our approach provided accurate predictions of future
glucose concentrations and good closed-loop performances of the overall MPC
controller.Comment: 10 pages, 5 Figures, 2 Table