do-mpc proposes a new, modularized implementation and testing support for optimal control schemes based on MPC approaches. The goal of this software project is to offer a simple to use and efficient platform, which allows users to define and test their problems very fast and trouble-free. In most cases, such implementations are highly complex and cumbersome, requiring considerable coding effort that only produces hardcoded solutions for each individual test case. With do-mpc we propose a generalized approach based on simple templates that can be edited for each individual problem. A robust and time efficient core module combines everything together automatically, such that the coding effort is reduced drastically. Taking advantage of state of the art third party software, do-mpc is able to handle a wide variety of problems, making even large systems real time feasible.

Moreover, do-mpc provides a very simple framework for the implementation of a state-of-the art robust nonlinear model predictive control approach called multi-stage NMPC, which is based on the description of the uncertainty as a scenario tree.

The do-mpc software is Python based and works therefore on any OS with a Python 2.7 distribution. do-mpc has been developed at the DYN chair of the TU Dortmund by Sergio Lucia and Alexandru Tatulea.

For detailed instructions go to the wiki

Go here for a list of the most frequently asked questions

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, ...)