文章基本信息

标题：RaPId - A Parameter Estimation Toolbox for Modelica/FMI-Based Models Exploiting Global Optimization Methods
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作者：Meaghan Podlaski ; Luigi Vanfretti ; Tetiana Bogodorova 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2021
卷号：54
期号：7
页码：391-396
DOI：10.1016/j.ifacol.2021.08.391
语种：English
出版社：Elsevier
摘要：AbstractThis 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.
关键词：KeywordsDynamic modelsElectric power systemsGlobal optimizationParameter estimationSoftware tools