Multivariable identification based MPC for closed-loop glucose regulation subject to individual variability

Abstract

The controller is important for the artificial pancreas to guide insulin infusion in diabetic therapy. However, the inter- and intra-individual variability and time delay of glucose metabolism bring challenges to control glucose within a normal range. In this study, a multivariable identification based model predictive control (mi-MPC) is developed to overcome the above challenges. Firstly, an integrated glucose-insulin model is established to describe insulin absorption, glucose-insulin interaction under meal disturbance, and glucose transport. On this basis, an observable glucose-insulin dynamic model is formed, in which the individual parameters and disturbances can be identified by designing a particle filtering estimator. Next, embedded with the identified glucose-insulin dynamic model, a mi-MPC method is proposed. In this controller, plasma glucose concentration (PGC), an important variable and indicator of glucose regulation, is estimated and controlled directly. Finally, the method was tested on 30 in-silico subjects produced by the UVa/Padova simulator. The results show that the mi-MPC method including the model, individual identification, and the controller can regulate glucose with the mean value of 7.45 mmol/L without meal announcement.

Keywords:

• Artificial pancreas
• particle filter
• multivariable identification
• model predictive control

Acknowledgement

We would like to acknowledge the access to the UVA/Padova simulator for research purposes.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

The authors also acknowledge the funding support from the National Natural Science Foundation of China (Grant Nos. 52205065, 62076177 and 62303030), the fellowship of China Postdoctoral Science Foundation (Grant No. 2022M710305), Beijing Advanced Innovation Center for Big Data-based Precision Medicine, and Key Medical Scientific Research Program of Shanxi Province (Grant No. 2021XM23).