Noise Handling in Data-Driven Predictive Control: A Strategy Based on Dynamic Mode Decomposition
Abstract
A major issue when exploiting data for direct control design is noise handling, since overlooking or improperly treating noise might have a catastrophic impact on closed-loop performance. Nonetheless, standard approaches to mitigate its effect might not be easily applicable for data-driven control design, since they often require tuning a set of hyper-parameters via potentially unsafe closed-loop experiments. By focusing on data-driven predictive control, we propose a noise handling approach based on truncated dynamic mode decomposition, along with an automatic tuning strategy for its hyper-parameters. By leveraging on pre-processing only, the proposed approach allows one to avoid dangerous closed-loop calibrations while being effective in coping with noise, as illustrated on a benchmark simulation example.
Cite
Text
Sassella et al. "Noise Handling in Data-Driven Predictive Control: A Strategy Based on Dynamic Mode Decomposition." Proceedings of The 4th Annual Learning for Dynamics and Control Conference, 2022.Markdown
[Sassella et al. "Noise Handling in Data-Driven Predictive Control: A Strategy Based on Dynamic Mode Decomposition." Proceedings of The 4th Annual Learning for Dynamics and Control Conference, 2022.](https://mlanthology.org/l4dc/2022/sassella2022l4dc-noise/)BibTeX
@inproceedings{sassella2022l4dc-noise,
title = {{Noise Handling in Data-Driven Predictive Control: A Strategy Based on Dynamic Mode Decomposition}},
author = {Sassella, Andrea and Breschi, Valentina and Formentin, Simone},
booktitle = {Proceedings of The 4th Annual Learning for Dynamics and Control Conference},
year = {2022},
pages = {74-85},
volume = {168},
url = {https://mlanthology.org/l4dc/2022/sassella2022l4dc-noise/}
}