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CITATION.cff
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CITATION.cff
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# This CITATION.cff file was generated with cffinit.
# Visit https://bit.ly/cffinit to generate yours today!
cff-version: 1.2.0
title: do-mpc
message: >-
If you use this software, please cite it using the
metadata from this file.
type: software
authors:
- orcid: 'https://orcid.org/0000-0003-3490-1256'
given-names: Felix
family-names: Fiedler
email: felix.fiedler@tu-dortmund.de
affiliation: >-
Chair of Process Automation Systems, TU Dortmund
University
- given-names: 'Benjamin '
family-names: Karg
email: benjamin.karg@tu-dortmund.de
affiliation: >-
Chair of Process Automation Systems, TU Dortmund
University
orcid: 'https://orcid.org/0000-0002-9779-3101'
- given-names: Lukas
family-names: Lüken
email: lukas.lueken@tu-dortmund.de
affiliation: >-
Chair of Process Automation Systems, TU Dortmund
University
orcid: 'https://orcid.org/0009-0004-5599-8282'
- given-names: Dean
family-names: Brandner
email: dean.brandner@tu-dortmund.de
affiliation: >-
Chair of Process Automation Systems, TU Dortmund
University
orcid: 'https://orcid.org/0000-0003-1500-7064'
- given-names: Moritz
family-names: Heinlein
email: moritz.heinlein@tu-dortmund.de
affiliation: >-
Chair of Process Automation Systems, TU Dortmund
University
orcid: 'https://orcid.org/0000-0002-2476-5919'
- given-names: Felix
family-names: Brabender
email: felix.brabender@tu-dortmund.de
affiliation: >-
Chair of Process Automation Systems, TU Dortmund
University
orcid: 'https://orcid.org/0009-0007-0737-5998'
- given-names: Sergio
family-names: Lucia
email: sergio.lucia@tu-dortmund.de
affiliation: >-
Chair of Process Automation Systems, TU Dortmund
University
orcid: 'https://orcid.org/0000-0002-3347-5593'
identifiers:
- type: doi
value: 10.1016/j.conengprac.2023.105676
description: The DOI of the encompassing paper.
repository-code: 'https://github.com/do-mpc/do-mpc'
url: 'https://www.do-mpc.com/en/latest/index.html'
abstract: >-
do-mpc is a comprehensive open-source toolbox for robust
model predictive control (MPC) and moving horizon
estimation (MHE). do-mpc enables the efficient formulation
and solution of control and estimation problems for
nonlinear systems, including tools to deal with
uncertainty and time discretization. The modular structure
of do-mpc contains simulation, estimation and control
components that can be easily extended and combined to fit
many different applications.
keywords:
- Nonlinear model predictive control
- Learning-based control
- Robust control
license: LGPL-3.0
preferred-citation:
type: article
authors:
- family-names: "Fiedler"
given-names: "Felix"
orcid: "https://orcid.org/0000-0003-3490-1256"
- family-names: "Karg"
given-names: "Benjamin "
orcid: "https://orcid.org/0000-0002-9779-3101"
- family-names: "Lüken"
given-names: "Lukas"
orcid: "https://orcid.org/0009-0004-5599-8282"
- family-names: "Brandner"
given-names: "Dean"
orcid: "https://orcid.org/0000-0003-1500-7064"
- family-names: "Heinlein"
given-names: "Moritz"
orcid: "https://orcid.org/0000-0002-2476-5919"
- family-names: "Brabender"
given-names: "Felix"
orcid: "https://orcid.org/0009-0007-0737-5998"
- family-names: "Sergio"
given-names: "Lucia"
orcid: "https://orcid.org/0000-0002-3347-5593"
doi: "10.1016/j.conengprac.2023.105676"
journal: "Control Engineering Practice"
month: 11
title: "do-mpc: Towards FAIR nonlinear and robust model predictive control"
volume: 140
year: 2023