Design of Bi-hormonal artificial pancreas system using switching economic model predictive control

Blood glucose management is a daily challenge that diabetics must face. The artificial pancreas (AP) system, especially the bi-hormonal type, is regarded as a hopeful way to achieve automated regulation of blood glucose (BG). At present, the existing design of AP mainly focuses on the control performance. However, the decrease of cure costs is also important in clinical practice. In this study, economic model predictive control based on particle swarm optimization is used to regulate the delivery rates of insulin and glucagon for the bi-hormonal AP system, in which an economic cost function is designed to achieve the switch logic between insulin subsystem and glucagon subsystem. The proposed method is compared with switching PID and switching model predictive control. The simulation results show that the proposed algorithm has excellent control performance and robustness. Specially, it can reduce the economic costs dramatically.