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:
The do-mpc software is Python based and works therefore on any OS with a Python 3.x distribution. do-mpc was originally 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 Chair of Process Automation Systems (PAS) of the TU Dortmund by Felix Brabender, Felix Fiedler and Sergio Lucia.
Installation instructions are given here.
Please visit our extensive documentation, kindly hosted on readthedocs.
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, ...)
FAQs
Unknown package
We found that do-mpc demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
Did you know?
Socket for GitHub automatically highlights issues in each pull request and monitors the health of all your open source dependencies. Discover the contents of your packages and block harmful activity before you install or update your dependencies.
Security News
PyPI confirms no security flaws were exploited in the Ultralytics supply chain attack and highlights improvements for safer package publishing.
Security News
Research
The Socket Research Team breaks down a malicious wrapper package that uses obfuscation to harvest credentials and exfiltrate sensitive data.
Research
Security News
Attackers used a malicious npm package typosquatting a popular ESLint plugin to steal sensitive data, execute commands, and exploit developer systems.
