pierre-haessig / python-dmpc

simulation tool for Model Predictive Control (MPC) and Distributed MPC, in Python

dmpc

Project adress: https://github.com/pierre-haessig/python-dmpc

Purpose

dmpc is simulation tool for Model Predictive Control (MPC) and Distributed MPC, written in pure Python.

As of now, it is in a very early stage, meaning that only a few subset of features are implemented (one type of MPC). However, what is implemented should work well enough and be covered by a resonable set of tests.

Installation

Beyond the usual stack of scientific Python packages (in fact only numpy), dmpc requires the optimization package cvxopt (http://cvxopt.org/) to solve linear or quadratic optimization problems.

To install dmpc directly from the source tree:

pip install git+git://github.com/pierre-haessig/python-dmpc.git

or clone the repository first and then run:

pip install .

Examples

Some examples are provided in the source tree.

As of now, only a heating example with a quadratic cost for the temperature deviations is implemented.