Constrained Linear Model Predictive Control for an Artificial Pancreas

The achievement of closed-loop glycemic control, and hence the automation of insulin administration, is highly required in the implementation of an Artificial Pancreas system. Various control theory approaches have been employed in numerous attempts to design an appropriate control algorithm. In our study, we adopted a Constrained linear model predictive controller (CLMPC). Our simulations have shown that the Constrained LMPC algorithm performs exceptionally well in maintaining blood glucose levels for patients across different scenarios. It effectively manages measurement and process noise, which is crucial for the practical implementation of the system. Moreover, the incorporation of feedforward capability in the controller has shown significant benefits, enabling proactive planning for future meals and optimizing blood glucose levels. Our CLMPC is more effective and very simple than the one presented in the literature, making it suitable for simple smartphone applications that support diabetes self-management.