Improvements in technology have allowed researchers to develop artificial pancreas systems (APS) that mimic glucose regulation function of a healthy pancreas. This study aims to develop a closed-loop APS to control blood glucose level (BGL) of diabetic rats in vivo. To this end, we developed a customized APS with a control algorithm based on model predictive control (MPC). MPC is widely used in many studies to determine the optimal insulin infusion based on BGL projection. We developed a time-series artificial neural network (ANN) to make multistep ahead predictions for MPC based on past BGL, insulin injection, and food intake. The objective of the ANN-based MPC (NN-MPC) is to keep BGL of the rats in vivo around a target value and maintain it within a normal range. The NN-MPC was tested in 24-hour experiments with type 1 diabetic rats monitored and controlled by our customized APS. For evaluation, the performance of the NN-MPC was compared with that of the state-of-the-art OpenAPS. Experimental results show that NN-MPC outperformed OpenAPS in terms of time within the normal range (88.47% vs. 71.82%) and variation of BGL. The customized APS with NN-MPC showed promising results for continuous, real-time control of BGL. The system can provide a testbed platform for closed-loop control where various control schemes and parameter tuning can be developed and validated.
Artificial neural network, Artificial pancreas system, Blood glucose level control, Model predictive control, Type 1 diabetes mellitus