control 0.10.2

Have a go now!

Try out the examples in the examples folder using the binder service.

The package can also be installed on Google Colab using the commands:

%pip install control
import control as ct

Features

• Linear input/output systems in state-space and frequency domain

• Block diagram algebra: serial, parallel, feedback, and other interconnections

• Time response: initial, step, impulse

• Frequency response: Bode, Nyquist, and Nichols plots

• Control analysis: stability, reachability, observability, stability margins, root locus

• Control design: eigenvalue placement, linear quadratic regulator, sisotool, hinfsyn, rootlocus_pid_designer

• Estimator design: linear quadratic estimator (Kalman filter)

• Nonlinear systems: optimization-based control, describing functions, differential flatness

Links

• Project home page: https://python-control.org

• Source code repository: https://github.com/python-control/python-control

• Documentation: https://python-control.readthedocs.io/

• Issue tracker: https://github.com/python-control/python-control/issues

• Mailing list: https://sourceforge.net/p/python-control/mailman/

Dependencies

The package requires numpy, scipy, and matplotlib. In addition, some routines use a module called slycot, that is a Python wrapper around some FORTRAN routines. Many parts of python-control will work without slycot, but some functionality is limited or absent, and installation of slycot is recommended (see below). The Slycot wrapper can be found at:

https://github.com/python-control/Slycot

Conda and conda-forge

The easiest way to get started with the Control Systems library is using Conda.

The Control Systems library has packages available using the conda-forge Conda channel, and as of Slycot version 0.3.4, binaries for that package are available for 64-bit Windows, OSX, and Linux.

To install both the Control Systems library and Slycot in an existing conda environment, run:

conda install -c conda-forge control slycot

Mixing packages from conda-forge and the default conda channel can sometimes cause problems with dependencies, so it is usually best to instally NumPy, SciPy, and Matplotlib from conda-forge as well.

Pip

To install using pip:

pip install slycot   # optional; see below
pip install control

If you install Slycot using pip you’ll need a development environment (e.g., Python development files, C and Fortran compilers). Pip installation can be particularly complicated for Windows.

Installing from source

To install from source, get the source code of the desired branch or release from the github repository or archive, unpack, and run from within the toplevel python-control directory:

pip install .

Code

You can check out the latest version of the source code with the command:

git clone https://github.com/python-control/python-control.git

Testing

You can run the unit tests with pytest to make sure that everything is working correctly. Inside the source directory, run:

pytest -v

or to test the installed package:

pytest --pyargs control -v

License

This is free software released under the terms of the BSD 3-Clause License. There is no warranty; not even for merchantability or fitness for a particular purpose. Consult LICENSE for copying conditions.

When code is modified or re-distributed, the LICENSE file should accompany the code or any subset of it, however small. As an alternative, the LICENSE text can be copied within files, if so desired.

Contributing

Your contributions are welcome! Simply fork the GitHub repository and send a pull request.

Please see the Developer’s Wiki for detailed instructions.