Control Systems Library for Python
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.
- 29 May 2010: Release of control-0.3a.tar.gz. Block diagram algebra for SISO state space systems is now implemented. New options in bode() for setting units to dB and Hz.
- 4 Jan 2010: Moved the subversion repository to SourceForge; Gunnar Ristroph is now helping out with the development.
- 2 Oct 2009: Kristian Soltesz at the Department of Automatic Control in Lund is working on developing a wrapper for SLICOT and also sponsoring some masters projects to develop python-based control systems tools.
- 10 Oct 2009: Release of control-0.2.tar.gz. Support block diagram algebra on SISO transfer functions plus Nyquist and Bode plots for state space and transfer function objects. Very rough.
- 10 Oct 2009: Set up a sourceforge project for the library: http://sourceforge.net/projects/python-control. Nothing downloaded yet, but might use this for shared development at some point.
- 30 May 2009: Test release of control-0.1.tar.gz just to see if the pieces are there
The current plan for the library is to implement single input, single output (SISO) transfer functions based on the signal.lti class in scipy, but use a separate class structure for state space objects, which will support multi-input, multi-output (MIMO) systems. There are a couple of reasons for this choice:
- The current LTI support in scipy only allows single input, multiple output systems. Hence it will not be possible to share any MIMO functionality with the signal library. Since there are very few uses of SIMO systems in controls, only SISO systems will be supported using the signal.lti object structure.
- As a first cut, I plan to focus on state-space computations and so it makes sense to go ahead and put MIMO functionality here for now. At a later date, it may make sense to add MIMO transfer functions (creating a new class).
I'm using the IPython environment, with SciPy extensions for scientific computing plus the matplotlib extensions (which enables MATLAB-like plotting). I am doing all of my playing on OS X, using fink.
Here's what I had to do to get the basic setup that I am using.
- Install SciPy - I did this using fink. Have to use the main/unstable tree.
- Install matplotlib - Need this for plotting. Also requires the main/unstable tree.
- Install ipython - interactive python interface. For python 2.5, I had to use the main/unstable tree.
Small snippet of code for testing if everything is installed
from scipy import * from matlibplot import * a = zeros(1000) a[:100]=1 b = fft(a) plot(abs(b)) show() # Not needed if you use ipython -pylab
Standard python package installation:
python setup.py install
To see if things are working, you can run the script secord-matlab.py (using ipython -pylab). It should generate a step repsonse, Bode plot and Nyquist plot for a simple second order linear system.
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.
- Basic constructors: ss(A, B, C, D), tf2ss, zpk2ss, iosys(f, h)
- Interconnections: series (*), parallel (+), feedback
- Properties: ctrb, obsv, pole, zero
- Frequency plots: bode, nyquist, rlocus, pzmap
- Simulations: iosim, lsim, step, impulse, initial
- Margins: margin
- Basic controllers: pid, lead, lag, leadlag
- State space: place, lqr, kalman, estim, reg
- SciPy.org - main web site for SciPy
- PyRo - Python Robotics library (might be nice to be compatible with this
- SLICOT - Fortran 77 implementations of numerical algorithms for computations in systems and control theory
- Octave Control Systems Toolbox - documentation for the Octave implementation (not sure what code is used for computing results)
- control.py - Python Module for System Dynamics and Controls by Ryan Krauss
- pycontrolsystems - python library to study control systems by rhafao (not sure if this is still active)
This is a fairly sporatic account of things I worked on, mainly so I can document problems that I came up against.
RMM: 10 Oct 09: release 0.2 for people to play with
- First cut implementation of transfer function operations, including composition and frequency domain plots
- Working on state space representation, including conversion and mixed operations with transfer functions plus frequency domain plots. Only SISO operations for now, and things aren't very complete yet.
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 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