We present the design and in-silico evaluation of a multi-step-ahead-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–output 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 four 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, a case with ± 25% decrease in the insulin sensitivity and a case with ± 25% error on the estimated meal amounts. 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 47.78 ± 8.55 vs. 34.62 ± 9.04, while the mean ± standard deviation percent time in severe hypoglycemia, i.e., < 54 [mg/dl] was 1.00 ± 3.18 vs. 9.45 ± 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.
Model Predictive Control (MPC) of an artificial pancreas with data-driven learning of multi-step-ahead blood glucose predictors
Aiello, E. M.;
2024-01-01
Abstract
We present the design and in-silico evaluation of a multi-step-ahead-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–output 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 four 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, a case with ± 25% decrease in the insulin sensitivity and a case with ± 25% error on the estimated meal amounts. 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 47.78 ± 8.55 vs. 34.62 ± 9.04, while the mean ± standard deviation percent time in severe hypoglycemia, i.e., < 54 [mg/dl] was 1.00 ± 3.18 vs. 9.45 ± 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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
