Non-Conservative Design of Robust Tracking Controllers Based on Input-Output Data
Abstract
This paper studies worst-case robust optimal tracking using noisy input-output data. We utilize behavioral system theory to represent system trajectories, while avoiding explicit system identification. We assume that the recent output data used in the data-dependent representation are noisy and we provide a non-conservative design procedure for robust control based on optimization with a linear cost and LMI constraints. Our methods rely on the parameterization of noise sequences compatible with the data-dependent system representation and on a suitable reformulation of the performance specification, which further enable the application of the S-lemma to derive an LMI optimization problem. The performance of the new controller is discussed through simulations.
Cite
Text
Xu et al. "Non-Conservative Design of Robust Tracking Controllers Based on Input-Output Data." Proceedings of the 3rd Conference on Learning for Dynamics and Control, 2021.Markdown
[Xu et al. "Non-Conservative Design of Robust Tracking Controllers Based on Input-Output Data." Proceedings of the 3rd Conference on Learning for Dynamics and Control, 2021.](https://mlanthology.org/l4dc/2021/xu2021l4dc-nonconservative/)BibTeX
@inproceedings{xu2021l4dc-nonconservative,
title = {{Non-Conservative Design of Robust Tracking Controllers Based on Input-Output Data}},
author = {Xu, Liang and Turan, Mustafa Sahin and Guo, Baiwei and Ferrari-Trecate, Giancarlo},
booktitle = {Proceedings of the 3rd Conference on Learning for Dynamics and Control},
year = {2021},
pages = {138-149},
volume = {144},
url = {https://mlanthology.org/l4dc/2021/xu2021l4dc-nonconservative/}
}