Metadata-Version: 2.1
Name: do_mpc
Version: 4.3.2
Summary: UNKNOWN
Home-page: https://www.do-mpc.com
Author: Sergio Lucia and Felix Fiedler
Author-email: sergio.lucia@tu-berlin.de
License: GNU Lesser General Public License version 3
Platform: UNKNOWN
Description-Content-Type: text/markdown
License-File: LICENSE.txt

<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

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**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, ...)


