Uncertainties and Modeling Errors of Type 1 Diabetes Models

  • Levente Kovács
  • Péter Szalay
Part of the Lecture Notes in Bioengineering book series (LNBE)


Modeling and control are tightly connected if we want to guarantee safety and reliability. These are minimum requirements in the medical field. The more sophisticated methods usually require information beyond the available measurements, and one way or another incorporate all a priori knowledge. This can manifest in state estimation, model-based prediction, or robust design assuming the worst case, among others. The better the model the better the achievable control; however, all aspects of modeling are more difficult in the case of physiological systems compared to regular engineering applications. In the following, we will investigate how various errors resulting from modeling inaccuracies affect the prediction of the behavior in case of blood glucose prediction. Sigma-point filters are used to efficiently support Kalman filtering, while the error sources are introduced in a single uncertainty block.


Kalman Filter Extend Kalman Filter Model Predictive Control Continuous Glucose Monitor Unscented Kalman Filter 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



Levente Kovács is Bolyai Fellow of the Hungarian Academy of Sciences. The work is partially supported by the Hungarian National Development Agency GOP-1.1.1.-11-2012-0055 project and by the European Union TÁMOP-4.2.2.A-11/1/KONV-2012-0073 project.


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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Physiological Controls Group, Applied Informatics Institute, John von Neumann Faculty of InformaticsObuda UniversityBudapestHungary
  2. 2.Department of Control Engineering and Information TechnologyBudapest University of Technology and EconomicsBudapestHungary

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