Python Control Systems Library (python-control)
The Python Control Systems Library, python-control, is a python package that implements basic operations for analysis and design of feedback control systems.
Announcements
- 21 Feb 2021 (RMM): development of the Python Control Toolbox has moved to GitHub (a while ago...)
- 18 Oct 2015 (RMM): uploaded version 0.7.0 to SourceForge. Installation using pip and conda is now supported:
** pip install slycot
** pip install control
- 9 Aug 2014 (RMM): moved developer support to github; file download, mailing lists and user discussions remain on SourceForge
- 4 Sep 2012 (RMM): removed MediaWiki hosted app. All information has been transferred to SF wiki (developer info) or (temporarily) to RMMWiki at Caltech (user information and examples). Will eventually copy user information to static project web page (high level) and Sphinx documentation.
- 3 Sep 2012 (RMM): porting mediawiki information to internal wiki + external sites (in preparation for removal of SourceForge hosted apps)
- 2 Sep 2012 (RMM): python-control has been upgraded to the new SourceForge platform
- 7 Aug 2011 (RMM): Version 0.5a has been released: release notes, file download
Project Overview
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 Åström and Murray. A MATLAB compatibility package (control.matlab) is available that provides functions corresponding to the commands available in the MATLAB Control Systems Toolbox.
Here are some of the basic functions that are (or will be) available in the package:
- Linear input/output systems in state space and frequency domain (transfer functions)
- Block diagram algebra: serial, parallel and feedback interconnections
- Time response: initial, step, impulse (using the scipy.signal package)
- Frequency response: Bode and Nyquist plots
- Control analysis: stability, reachability, observability, stability margins
- Control design: eigenvalue placement, linear quadratic regulator
- Estimator design: linear quadratic estimator (Kalman filter)
Project Admins: