Adaptive personalized prior-knowledge-informed model predictive control for type 1 diabetes. (February 2023)

• Record Type: Journal Article
• Authors: Sun, Xiaoyu; Rashid, Mudassir; Askari, Mohammad Reza; Cinar, Ali
• Is Part Of: Control engineering practice. Volume 131(2023)
• Publication Date: 2023-02
• Subjects: Model predictive control -- Adaptive control -- Partial least squares -- Prior knowledge -- Artificial pancreas -- Type 1 diabetes; Automatic control -- Periodicals; 629.89
• DOI: 10.1016/j.conengprac.2022.105386
• Languages: English
• ISSNs: 0967-0661

Abstract

Abstract: This work considers the problem of adaptive prior-informed model predictive control (MPC) formulations that explicitly incorporate prior knowledge in the model development and is robust to missing data in the output measurements. The proposed prediction model is based on a latent variables model to extract glycemic dynamics from highly-correlated data and incorporates prior knowledge of exponential stability to improve the prediction ability. Missing data structures are formulated to enable model predictions when output measurements are missing for short periods of time. Based on the latent variables model, the MPC strategy and adaptive rules are developed to automatically tune the aggressiveness of the MPC. The adaptive prior-knowledge-informed MPC is evaluated with computer simulations for the control of blood glucose concentrations in people with Type 1 diabetes (T1D) using simulated virtual patients. Due to the variability among people with T1D, the hyperparameters of the prior-knowledge-informed model are personalized to individual subjects. The percentage of time spent in the target range is 76.48% when there are no missing data and 76.52% when there are missing data episodes lasting up to 30 mins (6 samples). Incorporating the adaptive rules further improves the percentage of time in target range to 84.58% and 84.88% for cases with no missing data and missing data, respectively. The proposed adaptive prior-informed MPC formulation provides robust, effective, and safe regulation of glucose concentration in T1D despite disturbances and missing measurements. Highlights: Adaptive personalized MPC for management of type 1 diabetes (T1D). Presented prior-knowledge-informed glucose prediction model. For T1D it is highly beneficial to incorporate adaptive rules. Presented the MPC that is robust to missing data. Glycemic dynamic varies significantly from one individual to another.