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

• Published in: Computer methods in biomechanics and biomedical engineering Vol. 28; no. 1; pp. 37 - 50
• Main Authors: Wang, Weijie, Wang, Shaoping, Zhang, Yuwei, Geng, Yixuan, Li, Deng'ao, Liu, Shiwei
• Format: Journal Article
• Language: English
• Published: England Taylor & Francis 2025; Taylor & Francis Ltd
• Subjects: Artificial pancreas; Blood Glucose - metabolism; Closed loops; Computer Simulation; Controllers; Diabetes mellitus; Dynamic models; Glucose; Glucose metabolism; Glucose transport; Humans; Insulin; model predictive control; Models, Biological; Multivariable control; multivariable identification; Multivariate Analysis; Pancreas, Artificial; Parameter estimation; Parameter identification; particle filter; Predictive control; Variability; Artificial pancreas; model predictive control; multivariable identification; particle filter
• DOI: 10.1080/10255842.2023.2282952
• ISSN: 1025-5842; 1476-8259

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.