SIMBa (System Identification Methods leveraging Backpropagation) is an open-source toolbox leveraging Pytorch's Automatic Differentiation framework for stable state-space linear SysID. It allows the user to incorporate prior knowledge (like sparsity patterns of the state-space matrices) during the identification procedure.
SIMBa is available on pypi, it can be installed with pip install simbapy.
Alternatively, you can clone this github repository to use simba locally.
If matlab is installed on your machine, you can install matlabengine. If you are on the latest version of MATLAB, pip install matlabengine works, otherwise you might need to install an older version of matlabengine. See here the supported version of MATLAB.
SIMBa needs access the System Identification Toolbox and Symbolic Math Toolbox in MATLAB.
You can disable the use of matlab by overwriting IS_MATLAB in simba.parameters
SIMBa was first presented in Stable Linear Subspace Identification: A Machine Learning Approach and subsequently extended in SIMBa: System Identification Methods leveraging Backpropagation.
There seems to be a bug in the control library when the dimension of the control input is one, which raises an unwanted exception.
This does not affect SIMBa in general but can fail is SIPPY is required (e.g., for SIMBa's initialization).
To correct it, you'll need to go to timeresp.py in the library, typically located in your virtual environment at .venv/lib/python3.x/site-packages/control/ and add the following transpose at line 1002, before _check_convert_array:
if len(U.shape) > 1:
U = U.TThis project is jointly led by Loris Di Natale and Muhammad Zakwan, with the participation of Bratislav Svetozarevic, Philipp Heer, Giancarlo Ferrari Trecate, and Colin N. Jones.
Urban Energy Systems Lab, Empa, Switzerland
Laboratoire d'Automatique, EPFL, Switzerland
For more information, please contact loris.dinatale@alumni.epfl.ch