Dynamic optimization with CasADi
Författare
Summary, in English
We demonstrate how CasADi, a recently devel- oped, free, open-source, general purpose software tool for nonlinear optimization, can be used for dynamic optimization in a flexible, interactive and numerically efficient way.
CasADi is best described as a minimalistic computer al- gebra system (CAS) implementing automatic differentiation (AD) in eight different flavors. Similar to algebraic modeling languages like AMPL or GAMS, it includes high-level interfaces to state-of-the-art numerical codes for nonlinear program- ming, quadratic programming and integration of differential- algebraic equations. CasADi is implemented in self-contained C++ code and contains full-featured front-ends to Python and Octave for rapid prototyping.
In this paper, we discuss CasADi from the perspective of the developer or advanced user of algorithms for dynamic optimization for the first time, leaving out details on the implementation of the tool. We demonstrate how the tool can be used to model highly complex dynamical systems directly or import existing models formulated in the algebraic modeling language AMPL or the physical modeling language Modelica. Given this symbolic representation of the process models, the resulting optimal control problem can be solved using a vari- ety of methods, including transcription methods (collocation), methods with embedded integrators (multiple shooting) as well as indirect methods.
CasADi is best described as a minimalistic computer al- gebra system (CAS) implementing automatic differentiation (AD) in eight different flavors. Similar to algebraic modeling languages like AMPL or GAMS, it includes high-level interfaces to state-of-the-art numerical codes for nonlinear program- ming, quadratic programming and integration of differential- algebraic equations. CasADi is implemented in self-contained C++ code and contains full-featured front-ends to Python and Octave for rapid prototyping.
In this paper, we discuss CasADi from the perspective of the developer or advanced user of algorithms for dynamic optimization for the first time, leaving out details on the implementation of the tool. We demonstrate how the tool can be used to model highly complex dynamical systems directly or import existing models formulated in the algebraic modeling language AMPL or the physical modeling language Modelica. Given this symbolic representation of the process models, the resulting optimal control problem can be solved using a vari- ety of methods, including transcription methods (collocation), methods with embedded integrators (multiple shooting) as well as indirect methods.
Avdelning/ar
Publiceringsår
2012
Språk
Engelska
Sidor
681-686
Publikation/Tidskrift/Serie
IEEE 51st Annual Conference on Decision and Control (CDC), 2012
Dokumenttyp
Konferensbidrag
Förlag
IEEE - Institute of Electrical and Electronics Engineers Inc.
Ämne
- Control Engineering
Conference name
51st IEEE Conference on Decision and Control, 2012
Conference date
2012-12-10 - 2012-12-13
Conference place
Maui, Hawaii, United States
Status
Published
Projekt
- LCCC
Forskningsgrupp
- LCCC
ISBN/ISSN/Övrigt
- ISSN: 0743-1546
- ISBN: 978-1-4673-2065-8