Multivariable Recursive Subspace Identification with Application to Artificial Pancreas Systems

作     者：Hajizadeh I. Rashid M. Turksoy K. Samadi S. Feng J. Sevil M. Frantz N. Lazaro C. Maloney Z. Littlejohn E. Cinar A. 

作者机构：Department of Chemical and Biological Engineering Illinois Institute of Technology Chicago IL United States Department of Biomedical Engineering Illinois Institute of Technology Chicago IL United States Department of Electrical and Computer Engineering Illinois Institute of Technology Chicago IL United States Department of Pediatrics and Medicine Section of Endocrinology Kovler Diabetes Center University of Chicago Chicago IL United States 

出 版 物：《IFAC-PapersOnLine》 

年 卷 期：2017年第50卷第1期

页      面：886-891页

主　　题：Artificial pancreas Biomedical system identification Linear systems Modeling Recursive identification Subspace methods 

摘      要：Designing a fully automated artificial pancreas (AP) system is challenging. Changes in the glucose-insulin dynamics in the human body over time, and the inter-subject and day-to-day variability of people with type 1 diabetes (T1D) are two important factors that would highly undermine the performance of an AP that is based on time-invariant and non-individualized models. People with T1D show different responses to carbohydrate intake, insulin, physical activity and stress with day-to-day variability present between or within specific patients. Thus, the control law in an AP system requires a reliable time-varying individualized model to perform efficiently. In this work, a novel recursive identification approach called a Predictor-Based Subspace Identification (PBSID) method is used for identifying a linear time-varying glucose-insulin model for each individual. Model identification and validation are based on clinical data from closed-loop experiments. The models are evaluated by means of various performances indices: Variance Accounted For (VAF), Root mean square error (RMSE), Normalized root mean square error (NRMSE) and Normalized mean square error (NMSE). The proposed method provides a stable time-varying state space model over time. It can be also individualized for each patient by defining the order of the system correctly. The approach proposed in this work has shown a strong potential to identify a consistent glucose-insulin model in real time for use in an AP system. © 2017