<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:
F. Fiedler, B. Karg, L. Lüken, D. Brandner, M. Heinlein, F. Brabender and S. Lucia. do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice, 140:105676, 2023
Please remember to properly cite other software that you might be using too if you use **do-mpc** (e.g. CasADi, IPOPT, ...)
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