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Dynamic optimization with CasADi

Andersson, Joel; Åkesson, Johan LU and Diehl, Moritz (2012) 51st IEEE Conference on Decision and Control, 2012 In IEEE 51st Annual Conference on Decision and Control (CDC), 2012 p.681-686
Abstract
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... (More)
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. (Less)
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author
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
in
IEEE 51st Annual Conference on Decision and Control (CDC), 2012
pages
681 - 686
publisher
IEEE--Institute of Electrical and Electronics Engineers Inc.
conference name
51st IEEE Conference on Decision and Control, 2012
external identifiers
  • scopus:84874256978
ISSN
0743-1546
ISBN
978-1-4673-2065-8
DOI
10.1109/CDC.2012.6426534
project
LCCC
language
English
LU publication?
yes
id
a6c39f69-72a9-4502-87ea-fd581f67db4e (old id 2972265)
date added to LUP
2012-08-24 11:15:42
date last changed
2017-06-18 04:04:07
@inproceedings{a6c39f69-72a9-4502-87ea-fd581f67db4e,
  abstract     = {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.<br/><br>
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.<br/><br>
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.},
  author       = {Andersson, Joel and Åkesson, Johan and Diehl, Moritz},
  booktitle    = {IEEE 51st Annual Conference on Decision and Control (CDC), 2012},
  isbn         = {978-1-4673-2065-8},
  issn         = {0743-1546},
  language     = {eng},
  pages        = {681--686},
  publisher    = {IEEE--Institute of Electrical and Electronics Engineers Inc.},
  title        = {Dynamic optimization with CasADi},
  url          = {http://dx.doi.org/10.1109/CDC.2012.6426534},
  year         = {2012},
}