Jonas Gesenhues
Institute of Automatic Control, RWTH Aachen University, Germany
Marc Hein
Department of Anesthesiology, RWTH Aachen University Hospital, Germany
Maike Ketelhut
Institute of Automatic Control, RWTH Aachen University, Germany
Thivaharan Albin
Institute of Automatic Control, RWTH Aachen University, Germany
Dirk Abel
Institute of Automatic Control, RWTH Aachen University, Germany
Ladda ner artikel
http://dx.doi.org/10.3384/ecp17132613Ingår i: Proceedings of the 12th International Modelica Conference, Prague, Czech Republic, May 15-17, 2017
Linköping Electronic Conference Proceedings 132:68, s. 613-621
Publicerad: 2017-07-04
ISBN: 978-91-7685-575-1
ISSN: 1650-3686 (tryckt), 1650-3740 (online)
This paper presents a concept for online parameter identification intended to be used within cardiovascular research labs and hospitals of the future featuring a data network of medical sensors. It is based on iterative nonlinear optimization using a moving horizon scheme and object-oriented Modelica models. Special FMUs have been developed to interface the optimization module and the sensor hardware. The concept is demonstrated on an exemplary application of identifying the parameters of a model for the systemic circulation. Unlike classical online parameter identification methods, this concept allows for quickly implementing changes of the underlying model.
Online Parameter Identification, Moving Horizon, FMI, ModeliChart, JModelica.org, CasADi, Cardiovascular, Medical
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