Control Systems Library for Python

WARNING: The information on this page is out of date. The most up-to-date information about python-control is available in the documentation that is distributed with the package. python-control home on SourceForge

Python Control Systems Library (python-control)

Announcements The python-control user documentation has been shifted from SourceForge to MurrayWiki at Caltech. Developer documentation remains on SourceForge. Version 0.5a has been released: release notes, file download

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

• User manual - Sphinx documentation for the python-control package
• Download - download and install the latest release of the package
• Example: Vertical takeoff and landing aircraft - demonstration of package capabilities
• Announcements mailing list - sign up to receive announcements about python-control
• Project description page - summary of all project information (SourceForge)

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)