This repository is part of the supplementary material for the submission titled Improving the Performance of Robust Control through Event-Triggered Learning by Alexander von Rohr, Friedrich Solowjow and Sebastian Trimpe published in the proceedings of the IEEE Conference on Decision and Control.

If you are finding this code useful please get in contact and consider citing the paper.

@inproceedings{rohr2022improving,
  author = {{von Rohr}, Alexander and Solowjow, Friedrich and Trimpe, Sebastian},
  title = {Improving the Performance of Robust Control through Event-Triggered Learning},
  booktitle = {Proceedings of the IEEE Conference on Decision and Control},
  pages = {3424-3430},
  year = {2022},
  doi = {10.1109/CDC51059.2022.9993350},
}

This project uses pipenv (https://pypi.org/project/pipenv/) to manage dependencies I recommend using pyenv (https://github.com/pyenv/pyenv) to manage your python version.

When you have pipenv and the correct python version installed run

pipenv install

You als need to have MOSEK (https://www.mosek.com/) installed for the LMI based synthesis. We use PICOS (https://pypi.org/project/PICOS/) as interface to the underlying solver. That means, in principle, it is possible to replace MOSEK with CVXOPT (https://cvxopt.org/) without many changes.

Once you have installed all dependencies you can start the python virtual environment:

pipenv shell

The data presented in the paper is part of this repository and can be found in the data folder. To reproduce the figures presented in the paper you can re-run the scripts named plot_*.

The results of Section IV.A can be reproduced with the script cost_optimal_excitation_1d.py.

The results of Section IV.B can be reproduced with the script improve_robust_control.py.