Abstract
Current technological advances have brought closer to reality the project of a safe, portable, and efficient artificial pancreas for people with type 1 diabetes (T1D). Among the developed control strategies for T1D, model predictive control (MPC) has been emphasized in literature as a promising control for glucose regulation. However, these control strategies are commonly designed in a computer environment, regardless of the limitations of a portable device. In this paper, the performances of six embedded platforms and three open-source optimization solver algorithms are assessed for T1D treatment. Their advantages and limitations are clarified using four MPC formulations of increasing complexity and a hardware-in-the-loop methodology to evaluate glucose control in virtual adult subjects. The performance comparison includes the execution time, the difference concerning the evolution obtained in MATLAB, the processor temperature, energy consumption, time percentage in normoglycemia, and the number of hypo- and hyperglycemic events. Results show that Quadprog is the package that faithfully follows the results obtained with control strategies designed and tuned on a computer with the MATLAB software. In addition, the Raspberry Pi 3 and the Tinker Board S embedded systems present the appropriate characteristics to be implemented as portable devices in the artificial pancreas application according to the criteria set out in this work.
Highlights
Closed-loop glucose control, referred to as artificial pancreas, has emerged as the best solution to modulate insulin doses in response to blood glucose (BG) concentration in subjects with type 1 diabetes (T1D)
The same tolerances do not work well for all patients. These results suggest that the packages OSQP and CVXOPT are not reliable for T1D treatment, where the robustness and stability are of primal importance
The embedded systems and solver packages were tested with four model predictive control (MPC) strategies of increasing complexity
Summary
Closed-loop glucose control, referred to as artificial pancreas, has emerged as the best solution to modulate insulin doses in response to blood glucose (BG) concentration in subjects with type 1 diabetes (T1D). Artificial pancreas systems (APS) have been evaluated in clinical and home studies showing improved results than conventional sensor-augmented pump therapy. This extracorporeal device consists of a continuous glucose monitoring (CGM) system that provides. Analysis of Open Embedded APS glucose measurements at regular intervals, a control algorithm that processes the CGM information and computes the appropriate insulin dosage, and an insulin infusion pump to execute the control action All this process aims to emulate the natural behavior of the pancreas and provide a better quality of life to people with T1D [1, 2]. Adaptive control strategies have been formulated as the MPC with adaptive penalization functions for matrices Q, R [13] and the impulsive offset-free strategy with adaptive features introduced in [14] and [15]
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