Long-term prediction for T1DM model during state-feedback control

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

Avoiding low glucose concentration is critically important in type-1 diabetes treatment. Predicting the future plasma glucose levels could ensure the safety of the patient. However, such estimation is no trivial task. The current paper proposes a predictor framework which stems from Unscented Kalman filter and works during closed-loop control, that can predict hazardous glucose levels in advance. Once the blood glucose concentration starts to rise, the predictor activates and estimates future glucose levels up to 3 hours, confirming whether the controller can endanger the patient. The capabilities of the framework is presented through simulations based on the SimEdu validated in-silico simulator.

Original languageEnglish
Title of host publication12th IEEE International Conference on Control and Automation, ICCA 2016
PublisherIEEE Computer Society
Pages311-316
Number of pages6
ISBN (Electronic)9781509017386
DOIs
Publication statusPublished - Jul 7 2016
Event12th IEEE International Conference on Control and Automation, ICCA 2016 - Kathmandu, Nepal
Duration: Jun 1 2016Jun 3 2016

Publication series

NameIEEE International Conference on Control and Automation, ICCA
Volume2016-July
ISSN (Print)1948-3449
ISSN (Electronic)1948-3457

Other

Other12th IEEE International Conference on Control and Automation, ICCA 2016
CountryNepal
CityKathmandu
Period6/1/166/3/16

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Control and Systems Engineering
  • Electrical and Electronic Engineering
  • Industrial and Manufacturing Engineering

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  • Cite this

    Szalay, P., Benyo, Z., & Kovacs, L. (2016). Long-term prediction for T1DM model during state-feedback control. In 12th IEEE International Conference on Control and Automation, ICCA 2016 (pp. 311-316). [7505295] (IEEE International Conference on Control and Automation, ICCA; Vol. 2016-July). IEEE Computer Society. https://doi.org/10.1109/ICCA.2016.7505295