Data-driven control of nonlinear systems from input-output data

Autor:	Dai, Xiaoyan, De Persis, Claudio, Monshizadeh, Nima, Tesi, Pietro
Rok vydání:	2023
Předmět:	Electrical Engineering and Systems Science - Systems and Control Mathematics - Optimization and Control
Druh dokumentu:	Working Paper
DOI:	10.1109/CDC49753.2023.10384071
Popis:	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. Comment: 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
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2309.09208 Zobrazit plný text záznamu View this record from Arxiv