vbargsten / do-mpc

do-mpc

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, that 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.

Installation instructions

For detailed instructions go to the wiki

FAQ

Go here for a list of the most frequently asked questions