Non-conservative Design of Robust Tracking Controllers Based on Input-output Data

Liang Xu, Mustafa Sahin Turan, Baiwei Guo, Giancarlo Ferrari-Trecate
Proceedings of the 3rd Conference on Learning for Dynamics and Control, PMLR 144:138-149, 2021.

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 this Paper

BibTeX
@InProceedings{pmlr-v144-xu21a, 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}, pages = {138--149}, year = {2021}, editor = {Jadbabaie, Ali and Lygeros, John and Pappas, George J. and A. Parrilo, Pablo and Recht, Benjamin and Tomlin, Claire J. and Zeilinger, Melanie N.}, volume = {144}, series = {Proceedings of Machine Learning Research}, month = {07 -- 08 June}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v144/xu21a/xu21a.pdf}, url = {https://proceedings.mlr.press/v144/xu21a.html}, 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.} }
Endnote
%0 Conference Paper %T Non-conservative Design of Robust Tracking Controllers Based on Input-output Data %A Liang Xu %A Mustafa Sahin Turan %A Baiwei Guo %A Giancarlo Ferrari-Trecate %B Proceedings of the 3rd Conference on Learning for Dynamics and Control %C Proceedings of Machine Learning Research %D 2021 %E Ali Jadbabaie %E John Lygeros %E George J. Pappas %E Pablo A. Parrilo %E Benjamin Recht %E Claire J. Tomlin %E Melanie N. Zeilinger %F pmlr-v144-xu21a %I PMLR %P 138--149 %U https://proceedings.mlr.press/v144/xu21a.html %V 144 %X 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.
APA
Xu, L., Turan, M.S., Guo, B. & Ferrari-Trecate, G.. (2021). Non-conservative Design of Robust Tracking Controllers Based on Input-output Data. Proceedings of the 3rd Conference on Learning for Dynamics and Control, in Proceedings of Machine Learning Research 144:138-149 Available from https://proceedings.mlr.press/v144/xu21a.html.

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