文章基本信息

标题：Hybrid Online Sensor Error Detection and Functional Redundancy for Artificial Pancreas Control Systems
本地全文：下载
作者：Jianyuan Feng ; Kamuran Turksoy ; Sediqeh Samadi 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2016
卷号：49
期号：7
页码：753-758
DOI：10.1016/j.ifacol.2016.07.277
语种：English
出版社：Elsevier
摘要：Artificial pancreas (AP) control systems rely on signals from glucose sensors to collect glucose concentration (GC) information from people with Type 1 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 novel 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.
关键词：Functional sensor redundancysensor error detectionKalman filterlocally weighted partial least squares regressionglucose concentrationsartificial pancreas