do-mpc 4.0.0b1

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do-mpc: Robust optimal control toolbox

do-mpc proposes a new, modularized implementation for optimization based 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
• 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 at the DYN chair of the TU Dortmund by Sergio Lucia and Alexandru Tatulea. The development is continued at the IOT chair of the TU Berlin by Felix Fiedler and Sergio Lucia.

Installation instructions

Installation instructions are given here.

Documentation

Please visit our extensive documentation, 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, ...)