PythonVRFT Library - Version 0.0.5

VRFT Adaptive Control Library written in Python. Aim of this library is to provide an implementation of the VRFT (Virtual Reference Feedback Tuning) algorithm.

You can find the package also at the following link

Author: Alessio Russo (PhD Student at KTH - alesssior@kth.se)

License

Our code is released under the GPLv3 license (refer to the LICENSE file for details).

Requirements

To run the library you need atleast Python 3.5.

Other dependencies:

• NumPy (1.19.5)
• SciPy (1.6.0)

Installation

Check the requirements, but the following command should install all the packages. Run the following command from root folder:

pip install . 

Examples

Examples are located in the examples/ folder. At the moment there are examples available. Check example3 to see usage of instrumental variables.

Tests

To execute tests run the following command

python -m unittest

Changelog

• [V. 0.0.2][26.03.2017] Implement the basic VRFT algorithm (1 DOF. offline, linear controller, controller expressed as scalar product theta*f(z))
• [V. 0.0.3][05.01.2020] Code refactoring and conversion to Python 3; Removed support for Python Control library.
• [V. 0.0.5][08.01.2020] Add Instrumental Variables (IVs) Support
• [In Progress][07.01.2020-] Add Documentation and Latex formulas
• [TODO] Add MIMO Support
• [TODO] Generalize to other kind of controllers (e.g., neural nets)
• [TODO] Add Cython support

Citations

If you find this code useful in your research, please, consider citing it:

@misc{pythonvrft, author = {Alessio Russo}, title = {Python VRFT Library}, year = 2020, doi = {}, url = { https://github.com/rssalessio/PythonVRFT } }