Difference between revisions of "Control Systems Library for Python"

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{{warning|The information on this page is out of date.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.
This page collects some notes on a control systems library for Python.  The plan is to create an alternative to the MATLAB Control System Toolbox™ that can be used in courses and for research.  This page collects information about the toolbox, in preparation for actually writing some code.  If you stumble across this page and know of a similar package or would like to contribute, let me know.


Status updates:
'''[http://sourceforge.net/p/python-control/wiki/Home/ python-control home on SourceForge]'''
* 30  May 09: Test release of [http://www.cds.caltech.edu/~murray/software/control-0.1.tar.gz control-0.1.tar.gz] just to see if the pieces are there


== Architecture notes ==
}}


I'm trying to sort out the best object structure to use for control system objects.  There is already an LTI class in the signal processing module of SciPY, so I might be able to build on that (and hence get those systems for free).  Ideally, I'd like to allow for time delay in the linear systems representation, since these come up a lot in my group's research.
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<font color='blue' size='+2'>Python Control Systems Library (python-control)</font></td></tr>
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Here's some rough thoughts on a possible object structure:
<br>
* Option 1: build off of the 'signal.lti' object structure. This has the advantage of being compatible with existing 'signal' package functions. The signal.lti class currently represents systems simultaneously in transfer function, state space and "pzk" forms.  Not may functions written for it yet, though.
{| style="float: right" width=30% border=1
* Option 2: emulate the MATLAB systems structure.  This uses separate classes for transfer functions, state space, "pzk" and frequency response data (frd) forms.  There is also a state space format for delay systems, which seems quite useful.
|-
* Option 3: new structure that allows nonlinear computations and other general objects
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** One possibility here is to represent everything in the python-control library in state space and leave transfer functions for the signal library
==== Announcements ====
** Would like to do this in a way that keeps track of the state + dynamics + delays and then allow linear systems as a special case, with functions like 'trim', 'linmod', etc to determine linearizations
* The python-control user documentation has been shifted from SourceForge to MurrayWiki at Caltech.  Developer documentation remains on [http://python-control.sf.net SourceForge].
** Composition, feedback, etc can work by creating composite functions corresponding to series, parallel, feedback interconnections (need to think about this)
* Version 0.5a has been released: [http://sourceforge.net/mailarchive/message.php?msg_id=27912588 release notes], [http://sourceforge.net/projects/python-control/files/ file download]
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The Python Control Systems Library, python-control, is a python package that implements basic operations for analysis and design of feedback control systems.
* [http://python-control.sourceforge.net/manual User manual] - Sphinx documentation for the python-control package
* [[http:sourceforge.net/p/python-control/wiki/Download/|Download]] - download and install the latest release of the package
* [[python-control/Example: Vertical takeoff and landing aircraft|Example: Vertical takeoff and landing aircraft]] - demonstration of package capabilities
* [https://lists.sourceforge.net/lists/listinfo/python-control-announce Announcements mailing list] - sign up to receive announcements about python-control
<!-- * [[http:sourceforge.net/p/python-control/wiki/Developer%20information/|Developer information]] -  project information for active python-control developers -->
* [https://sourceforge.net/projects/python-control/ Project description page] - summary of all project information (SourceForge)


== Installation instructions ==
=== Project Overview ===


I'm using the [http://ipython.scipy.org/moin/ IPython] environment, with [http://scipy.org SciPy] extensions for scientific computing plus the [http://matplotlib.sourceforge.net/ matplotlib] extensions (which enables MATLAB-like plotting).  I am doing all of my playing on OS X, using fink.
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 ''[http://www.cds.caltech.edu/~murray/amwiki Feedback Systems]'' by &Aring;str&ouml;m and Murray.  A MATLAB compatibility package (control.matlab) is available that provides functions corresponding to the commands available in the MATLAB Control Systems Toolbox.


=== Prerequisites ===
Here are some of the basic functions that are (or will be) available in the package:
Here's what I had to do to get the basic setup that I am using.
* Linear input/output systems in state space and frequency domain (transfer functions)
# Install SciPy - I did this using fink.  Have to use the main/unstable tree.
* Block diagram algebra: serial, parallel and feedback interconnections
# Install matplotlib - Need this for plotting
* Time response: initial, step, impulse (using the scipy.signal package)
# Install ipython - interactive python interface
* Frequency response: Bode and Nyquist plots
 
* Control analysis: stability, reachability, observability, stability margins
Small snippet of code for testing if everything is installed
* Control design: eigenvalue placement, linear quadratic regulator
from scipy import *
* Estimator design: linear quadratic estimator (Kalman filter)
from matlibplot import *
a = zeros(1000)
a[:100]=1
b = fft(a)
plot(abs(b))
show()                # Not needed if you use ipython -pylab
 
=== python-control ===
Standard python package installation:
 
  python setup.py install
 
To see if things are working, you can run the script [http://www.cds.caltech.edu/~murray/software/python-control/examples/secord-matlab.py secord-matlab.py] (using ipython -pylab).  It should generate a step repsonse, Bode plot and Nyquist plot for a simple second order linear system.
 
== Functionality ==
 
This section contains a list of the functions that I plan to implement in the first few passes through the library.  This is mainly based on the functions that we use in CDS 110ab at Caltech, plus a few other functions that I think students are likely to wait to see for one reason or another.
 
=== Constructing systems ===
* Basic constructors: ss(A, B, C, D), tf2ss, zpk2ss, iosys(f, h)
* Interconnections: series (*), parallel (+), feedback
 
=== Analysis ===
* Properties: ctrb, obsv, pole, zero
* Frequency plots: bode, nyquist, rlocus, pzmap
* Simulations: iosim, lsim, step, impulse, initial
* Margins: margin
 
=== Synthesis ===
* Basic controllers: pid, lead, lag, leadlag
* State space: place, lqr, kalman, estim, reg
 
== Related documentation ==
 
=== Python documentation ===
* [http://www.scipy.org/ SciPy.org] - main web site for SciPy
** [http://ipython.scipy.org/moin/ IPython] - enhanced shell for python
**  [http://matplotlib.sourceforge.net/ matplotlib] - 2D plotting for python
* [http://www.pyrorobotics.org PyRo] - Python Robotics library (might be nice to be compatible with this
 
=== Related packages ===
* [http://www.slicot.org/ SLICOT] - Fortran 77 implementations of numerical algorithms for computations in systems and control theory
** [http://github.com/henjo/slicot/tree/master python wrapper] - Numpy wrapper of the control and systems library SLICOT
** [http://osdir.com/ml/python.scientific.devel/2004-09/msg00052.html python info] - Message giving information on making SLICOT available in python
* [http://www.gnu.org/software/octave/doc/interpreter/Control-Theory.html Octave Control Systems Toolbox] - documentation for the Octave implementation (not sure what code is used for computing results)
* [http://www.siue.edu/~rkrauss/python_intro.html control.py] - Python Module for System Dynamics and Controls by Ryan Krauss
 
== Activity Log ==
 
This is a fairly sporatic account of things I worked on, mainly so I can document problems that I came up against.
 
'''RMM: 28 May 09''': release 0.1 as a demo
* Updated to build off of signal.lti object structure (allows easy step responses)
* Put together an example + setup script, etc; available as [http://www.cds.caltech.edu/~murray/software/control-0.1.tar.gz control-0.1.tar.gz]
 
'''RMM: 28 May 09''': preliminary SLICOT functionality working
* Figured out enough about f2py to get the SLICOT function AB01MD working
* Main issue was sorting out the intent macros; the SLICOT python wrapper example had most of the clues
** Use 'intent(in,out)' for variables that are both inputs and outputs
** Use 'depend' to automatically create various arguments that can be derived from matrix inputs (dimensions, etc)
* Solve dependencies by adding functions one at a time
 
'''RMM: 27 May 09''': problems with SLICOT
* Having trouble getting f2py working correctly on SLICOT.  Errors in compilation
* Backed up to getting a "hello world" example working.  Finally got this to work after editing gnu.py in the numpy/distutils source to eliminate the cc_dynamic dependency (specifically enabled for darwin?)
f2py2.5 -h hello.pyf -m hello *.f
f2py2.5 -c -m hello *.f *.pyf
* Might have issues with g77 versus gfortran (FORTRAN 90); will probably need to selectively include SLICOT modules and get things working slowly

Latest revision as of 21:11, 31 May 2015

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.

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)
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