control 0.10.2
Python Control Systems Library
Requires Python: >=3.10

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Python Control Systems Library
==============================

The Python Control Systems Library is a Python module that implements basic
operations for analysis and design of feedback control systems.

Have a go now!
--------------
Try out the examples in the examples folder using the binder service.

.. image:: https://mybinder.org/badge_logo.svg
 :target: https://mybinder.org/v2/gh/python-control/python-control/HEAD

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


Installation
============

Conda and conda-forge
---------------------

The easiest way to get started with the Control Systems library is
using `Conda <https://conda.io>`_.

The Control Systems library has packages available using the `conda-forge
<https://conda-forge.org>`_ 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 .

Article and Citation Information
================================

An `article <https://ieeexplore.ieee.org/abstract/document/9683368>`_ about
the library is available on IEEE Explore. If the Python Control Systems Library helped you in your research, please cite::

  @inproceedings{python-control2021,
    title={The Python Control Systems Library (python-control)},
    author={Fuller, Sawyer and Greiner, Ben and Moore, Jason and
            Murray, Richard and van Paassen, Ren{\'e} and Yorke, Rory},
    booktitle={60th IEEE Conference on Decision and Control (CDC)},
    pages={4875--4881},
    year={2021},
    organization={IEEE}
  }

or the GitHub site: https://github.com/python-control/python-control

Development
===========

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

.. _pytest: https://docs.pytest.org/

License
-------

This is free software released under the terms of `the BSD 3-Clause
License <https://opensource.org/licenses/BSD-3-Clause>`_.  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`_.

.. _pull request: https://github.com/python-control/python-control/pulls

Please see the `Developer's Wiki`_ for detailed instructions.

.. _Developer's Wiki: https://github.com/python-control/python-control/wiki