Multivariable Recursive Subspace Identification with Application to Artificial Pancreas Systems. Issue 1 (July 2017)

• Record Type: Journal Article
• Authors: Hajizadeh, I.; Rashid, M.; Turksoy, K.; Samadi, S.; Feng, J.; Sevil, M.; Frantz, N.; Lazaro, C.; Maloney, Z.; Littlejohn, E.; Cinar, A.
• Is Part Of: IFAC-PapersOnLine. Volume 50:Issue 1(2017)
• Page Start: 886
• Page End: 891
• Publication Date: 2017-07
• Subjects: Modeling -- identification -- Artificial pancreas -- Biomedical system -- Subspace methods -- Recursive identification -- Linear systems; Automatic control -- Periodicals; 629.805
• DOI: 10.1016/j.ifacol.2017.08.268
• Languages: English
• ISSNs: 2405-8963

Abstract

Abstract: 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.