Hybrid Online Sensor Error Detection and Functional Redundancy for Artificial Pancreas Control Systems

Artificial pancreas (AP) control systems rely on signals from glucose sensors to collect glucose concentration (GC) information from people with Type I diabetes and compute insulin infusion rates to maintain GC within a desired range. Sensor performance is often limited by sensor errors, communication interruptions and noise. A hybrid online sensor error detection and functional redundancy system is developed to detect errors in online signals, and replace erroneous or missing values detected with model-based estimates. The proposed hybrid system relies on two techniques, an outlier-robust Kalman filter (ORKF) and a locally-weighted partial least squares (LW-PLS) regression model. This leverages the advantages of automatic measurement error elimination with ORKF and data-driven prediction with LW-PLS. A noel method called nominal angle analysis is proposed to distinguish between signal faults and large changes in sensor values caused by real dynamic changes in the metabolism. The performance of the system is illustrated with clinical data from continuous glucose monitoring sensors collected from people with Type 1 diabetes. (C) 2016; IFAC (International Federation a Automatic Control) Hosting by Elsevier Ltd. All rights reserved.