Welcome to the Python Control Systems Toolbox (python-control) User’s Manual. This manual contains information on using the python-control package, including documentation for all functions in the package and examples illustrating their use.

Overview of the toolbox

The python-control package is a set of python classes and functions that implement common operations for the analysis and design of feedback control systems. The initial goal is to implement all of the functionality required to work through the examples in the textbook Feedback Systems by Astrom and Murray. A MATLAB compatibility module is available that provides many of the common functions corresponding to commands available in the MATLAB Control Systems Toolbox.

Some differences from MATLAB

The python-control package makes use of NumPy and SciPy. A list of general differences between NumPy and MATLAB can be found here.

In terms of the python-control package more specifically, here are some thing to keep in mind:

  • You must include commas in vectors. So [1 2 3] must be [1, 2, 3].

  • Functions that return multiple arguments use tuples.

  • You cannot use braces for collections; use tuples instead.


The python-control package can be installed using pip, conda or the standard setuptools mechanisms. The package requires numpy and scipy, and the plotting routines require matplotlib. In addition, some routines require the slycot library in order to implement more advanced features (including some MIMO functionality).

To install using pip:

pip install slycot   # optional
pip install control

Many parts of python-control will work without slycot, but some functionality is limited or absent, and installation of slycot is recommended. Users can check to insure that slycot is installed correctly by running the command:

python -c "import slycot"

and verifying that no error message appears. More information on the slycot package can be obtained from the slycot project page.

For users with the Anaconda distribution of Python, the following commands can be used:

conda install numpy scipy matplotlib    # if not yet installed
conda install -c conda-forge control slycot

This installs slycot and python-control from conda-forge, including the openblas package.

Alternatively, to use setuptools, first download the source and unpack it. To install in your home directory, use:

python install --user

or to install for all users (on Linux or Mac OS):

python build
sudo python install

Getting started

There are two different ways to use the package. For the default interface described in Function reference, simply import the control package as follows:

>>> import control

If you want to have a MATLAB-like environment, use the MATLAB compatibility module:

>>> from control.matlab import *