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Conference Contribution

Derivative-free Parameter Optimization of Functional Mock-up Units

Christian Andersson, Sofia Gedda, Johan Åkesson, Stefan Diehl

Abstract

Representing a physical system with a mathematical model requires knowledge not only about the physical laws governing the dynamics but also about the parameter values of the system. The parameters can sometimes be measured or calculated, however some of them are often difficult or impossible to obtain in these ways. Finding accurate parameter values is crucial for the accuracy of the mathematical model.

Estimating the parameters using optimization algorithms which attempt to
minimize the error between the response from the mathematical model and the physical system is a common approach for improving the accuracy of the model.

Optimization algorithms usually requires information about the derivatives which may not always be available or not be appropriate for estimation which forces the use of derivative-free optimization algorithms.

In this paper, we present an implementation of derivative-free optimization algorithms for parameter estimation in the JModelica.org platform. The implementation allows the underlying dynamic system to be represented as a Functional Mock-up Unit (FMU), thus enables parameter estimation of models designed in modeling tools following the standardized interface, the Functional Mock-up Interface (FMI), such as Dymola.

Keywords

Derivative-free optimization Parameter Estimation JModelica.org FMI Assimulo


In 9th International Modelica Conference, Munich, Germany, September 2012.

 
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