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|
%global _empty_manifest_terminate_build 0
Name: python-do-mpc
Version: 4.6.0
Release: 1
Summary: please add a summary manually as the author left a blank one
License: GNU Lesser General Public License version 3
URL: https://www.do-mpc.com
Source0: https://mirrors.aliyun.com/pypi/web/packages/fe/2f/031e03849149aa72d51df399cd98b63477f34317fb10e1e6915cdedbbbec/do_mpc-4.6.0.tar.gz
BuildArch: noarch
Requires: python3-casadi
Requires: python3-numpy
Requires: python3-matplotlib
%description
<img align="left" width="30%" hspace="2%" src="https://raw.githubusercontent.com/do-mpc/do-mpc/master/documentation/source/static/dompc_var_02_rtd_blue.png">
# Model predictive control python toolbox
[](https://www.do-mpc.com)
[](https://github.com/do-mpc/do-mpc/actions/workflows/pythontest.yml)
[](https://badge.fury.io/py/do-mpc)
[](https://github.com/do-mpc/do-mpc)
**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.
In summary, **do-mpc** offers the following features:
* nonlinear and economic model predictive control
* support for differential algebraic equations (DAE)
* time discretization with orthogonal collocation on finite elements
* robust multi-stage model predictive control
* moving horizon state and parameter estimation
* modular design that can be easily extended
The **do-mpc** software is Python based and works therefore on any OS with a Python 3.x distribution. **do-mpc** has been developed by Sergio Lucia and Alexandru Tatulea at the DYN chair of the TU Dortmund lead by Sebastian Engell. The development is continued at the [Laboratory of Process Automation Systems](https://pas.bci.tu-dortmund.de) (PAS) of the TU Dortmund by Felix Fiedler and Sergio Lucia.
## Installation instructions
Installation instructions are given [here](https://www.do-mpc.com/en/latest/installation.html).
## Documentation
Please visit our extensive [documentation](https://www.do-mpc.com), kindly hosted on readthedocs.
## Citing **do-mpc**
If you use **do-mpc** for published work please cite it as:
S. Lucia, A. Tatulea-Codrean, C. Schoppmeyer, and S. Engell. Rapid development of modular and sustainable nonlinear model predictive control solutions. Control Engineering Practice, 60:51-62, 2017
Please remember to properly cite other software that you might be using too if you use **do-mpc** (e.g. CasADi, IPOPT, ...)
%package -n python3-do-mpc
Summary: please add a summary manually as the author left a blank one
Provides: python-do-mpc
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-do-mpc
<img align="left" width="30%" hspace="2%" src="https://raw.githubusercontent.com/do-mpc/do-mpc/master/documentation/source/static/dompc_var_02_rtd_blue.png">
# Model predictive control python toolbox
[](https://www.do-mpc.com)
[](https://github.com/do-mpc/do-mpc/actions/workflows/pythontest.yml)
[](https://badge.fury.io/py/do-mpc)
[](https://github.com/do-mpc/do-mpc)
**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.
In summary, **do-mpc** offers the following features:
* nonlinear and economic model predictive control
* support for differential algebraic equations (DAE)
* time discretization with orthogonal collocation on finite elements
* robust multi-stage model predictive control
* moving horizon state and parameter estimation
* modular design that can be easily extended
The **do-mpc** software is Python based and works therefore on any OS with a Python 3.x distribution. **do-mpc** has been developed by Sergio Lucia and Alexandru Tatulea at the DYN chair of the TU Dortmund lead by Sebastian Engell. The development is continued at the [Laboratory of Process Automation Systems](https://pas.bci.tu-dortmund.de) (PAS) of the TU Dortmund by Felix Fiedler and Sergio Lucia.
## Installation instructions
Installation instructions are given [here](https://www.do-mpc.com/en/latest/installation.html).
## Documentation
Please visit our extensive [documentation](https://www.do-mpc.com), kindly hosted on readthedocs.
## Citing **do-mpc**
If you use **do-mpc** for published work please cite it as:
S. Lucia, A. Tatulea-Codrean, C. Schoppmeyer, and S. Engell. Rapid development of modular and sustainable nonlinear model predictive control solutions. Control Engineering Practice, 60:51-62, 2017
Please remember to properly cite other software that you might be using too if you use **do-mpc** (e.g. CasADi, IPOPT, ...)
%package help
Summary: Development documents and examples for do-mpc
Provides: python3-do-mpc-doc
%description help
<img align="left" width="30%" hspace="2%" src="https://raw.githubusercontent.com/do-mpc/do-mpc/master/documentation/source/static/dompc_var_02_rtd_blue.png">
# Model predictive control python toolbox
[](https://www.do-mpc.com)
[](https://github.com/do-mpc/do-mpc/actions/workflows/pythontest.yml)
[](https://badge.fury.io/py/do-mpc)
[](https://github.com/do-mpc/do-mpc)
**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.
In summary, **do-mpc** offers the following features:
* nonlinear and economic model predictive control
* support for differential algebraic equations (DAE)
* time discretization with orthogonal collocation on finite elements
* robust multi-stage model predictive control
* moving horizon state and parameter estimation
* modular design that can be easily extended
The **do-mpc** software is Python based and works therefore on any OS with a Python 3.x distribution. **do-mpc** has been developed by Sergio Lucia and Alexandru Tatulea at the DYN chair of the TU Dortmund lead by Sebastian Engell. The development is continued at the [Laboratory of Process Automation Systems](https://pas.bci.tu-dortmund.de) (PAS) of the TU Dortmund by Felix Fiedler and Sergio Lucia.
## Installation instructions
Installation instructions are given [here](https://www.do-mpc.com/en/latest/installation.html).
## Documentation
Please visit our extensive [documentation](https://www.do-mpc.com), kindly hosted on readthedocs.
## Citing **do-mpc**
If you use **do-mpc** for published work please cite it as:
S. Lucia, A. Tatulea-Codrean, C. Schoppmeyer, and S. Engell. Rapid development of modular and sustainable nonlinear model predictive control solutions. Control Engineering Practice, 60:51-62, 2017
Please remember to properly cite other software that you might be using too if you use **do-mpc** (e.g. CasADi, IPOPT, ...)
%prep
%autosetup -n do_mpc-4.6.0
%build
%py3_build
%install
%py3_install
install -d -m755 %{buildroot}/%{_pkgdocdir}
if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
pushd %{buildroot}
if [ -d usr/lib ]; then
find usr/lib -type f -printf "\"/%h/%f\"\n" >> filelist.lst
fi
if [ -d usr/lib64 ]; then
find usr/lib64 -type f -printf "\"/%h/%f\"\n" >> filelist.lst
fi
if [ -d usr/bin ]; then
find usr/bin -type f -printf "\"/%h/%f\"\n" >> filelist.lst
fi
if [ -d usr/sbin ]; then
find usr/sbin -type f -printf "\"/%h/%f\"\n" >> filelist.lst
fi
touch doclist.lst
if [ -d usr/share/man ]; then
find usr/share/man -type f -printf "\"/%h/%f.gz\"\n" >> doclist.lst
fi
popd
mv %{buildroot}/filelist.lst .
mv %{buildroot}/doclist.lst .
%files -n python3-do-mpc -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Tue Jun 20 2023 Python_Bot <Python_Bot@openeuler.org> - 4.6.0-1
- Package Spec generated
|
