RaPId - A parameter estimation toolbox for modelica/FMI-based models exploiting global optimization methods

Meaghan Podlaski, Luigi Vanfretti, Tetiana Bogodorova, Tin Rabuzin, Maxime Baudette

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

2 Scopus citations


This paper describes new additions to the Rapid Parameter Identification Toolbox (RaPId), which is an open-source MATLAB toolbox for parameter estimation using models developed with the Modelica language and exported with the functional mock-up interface (FMI) Standard. These additions include an updated graphical user interface (GUI), an optimization method utilizing multiple starting points for a gradient descent optimization, and examples for different cyber-physical system applications such as the Duffing-Holmes equation modeling in a form of electrical circuit and a hydroelectric power plant modeling.
Original languageEnglish (US)
Title of host publication19th IFAC Symposium on System Identification, SYSID 2021
PublisherElsevier BV
Number of pages6
StatePublished - Sep 15 2021
Externally publishedYes

Bibliographical note

KAUST Repository Item: Exported on 2022-06-23
Acknowledgements: This work was supported in part by the National Aeronautics and Space Administration under award number 80NSSC19M0125 as part of the Center for High-Efficiency Electrical Technologies for Aircraft (CHEETA), by Dominion Energy, by the New York State Energy Research and Development Authority (NYSERDA) under agreement numbers 137940 and 137951, by the Engineering Research Center Program of the National Science Foundation and the Department of Energy under Award EEC-1041877, and in part by the Center of Excellence for NEOM Research at King Abdullah University of Science and Technology. The first author is supported by the National Science Foundation Graduate Research Fellowship Program under Grant No. DGE 1744655 and the Chateaubriand Fellowship of the Office for Science & Technology of the Embassy of France in the United States.
This publication acknowledges KAUST support, but has no KAUST affiliated authors.


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