Data-driven control of nonlinear systems from input-output data
Abstract
The design of controllers from data for nonlinear systems is a challenging problem. In a recent paper, De Persis, Rotulo and Tesi, "Learning controllers from data via approximate nonlinearity cancellation," IEEE Transactions on Automatic Control, 2023, a method to learn controllers that make the closed-loop system stable and dominantly linear was proposed. The approach leads to a simple solution based on data-dependent semidefinite programs. The method uses input-state measurements as data, while in a realistic setup it is more likely that only input-output measurements are available. In this note we report how the design principle of the above mentioned paper can be adjusted to deal with input-output data and obtain dynamic output feedback controllers in a favourable setting.
Cite
@article{arxiv.2309.09208,
title = {Data-driven control of nonlinear systems from input-output data},
author = {Xiaoyan Dai and Claudio De Persis and Nima Monshizadeh and Pietro Tesi},
journal= {arXiv preprint arXiv:2309.09208},
year = {2024}
}
Comments
Submitted for peer review on 31 March 2023. To appear in the Proceedings of the 62nd IEEE Conference on Decision and Control, 13-15 December 2023, Singapore