Dynamic Parametric Sensitivity Optimization Using Simultaneous Discretization in JModelica.org

Summary, in English

Dynamic optimization problems involving parametric sensitivities, such as optimal experimental design, are typically solved using shooting-based methods, while leveraging numerical integrators with sensitivity computation capabilities. In this paper we present how simultaneous discretization can be employed to solve these problems, by augmenting the dynamic optimization problems with forward sensitivity equations.



We present an implementation of this approach in the open-source, Modelica-based tool JModelica.org, which addresses the need for solving optimal experimental design problems in Modelica tools. The implementation is demonstrated on a fed-batch reactor and a plate-fin heat exchanger.

• Author: Fredrik Magnusson; Kyle Palmer; Lu Han; George Bollas
• Department/s: Department of Automatic Control; ELLIIT: the Linköping-Lund initiative on IT and mobile communication
• Publishing year: 2015
• Language: English
• Pages: 37-42
• Publication/Series: 2015 International Conference on Complex Systems Engineering
• Full text: - 250 kB; Download statistics
• Links: http://dx.doi.org/10.1109/ComplexSys.2015.7385980
• Document type: Conference paper
• Publisher: IEEE - Institute of Electrical and Electronics Engineers Inc.
• Topic: Control Engineering
• Conference name: 2015 International Conference on Complex Systems Engineering
• Conference date: 2015-11-09
• Status: Published
• Project: Numerical and Symbolic Algorithms for Dynamic Optimization; LCCC
• Research group: LCCC
• ISBN/ISSN/Other: ISBN: 9781467371797