  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 $\pm$ standard deviation percent time in the
range 70-180 [mg/dL] was 74.99 $\pm$ 7.09 vs. 54.15 $\pm$ 14.89, the mean $\pm$
standard deviation percent time in the tighter range 70-140 [mg/dL] was
47.78$\pm$8.55 vs. 34.62 $\pm$9.04, while the mean $\pm$ standard deviation
percent time in sever hypoglycemia, i.e., $<$ 54 [mg/dl] was 1.00$\pm$3.18 vs.
9.45$\pm$11.71, 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.
