Print Email Facebook Twitter Learning Control of Second-Order Systems via Nonlinearity Cancellation Title Learning Control of Second-Order Systems via Nonlinearity Cancellation Author Guo, M. (TU Delft Team Meichen Guo) De Persis, Claudio (Rijksuniversiteit Groningen) Tesi, Pietro (University of Florence) Date 2023 Abstract A technique to design controllers for nonlinear systems from data consists of letting the controllers learn the nonlinearities, cancel them out and stabilize the closed-loop dynamics. When control and nonlinearities are unmatched, the technique leads to an approximate cancellation and local stability results are obtained. In this paper, we show that, if the system has some structure that the designer can exploit, an iterative use of the data leads to a globally stabilizing controller even when control and nonlinearities are unmatched. To reference this document use: http://resolver.tudelft.nl/uuid:f97e7b2e-108a-4f5e-8a78-68ef31d1375c DOI https://doi.org/10.1109/CDC49753.2023.10383435 Publisher IEEE Embargo date 2024-07-19 ISBN 979-8-3503-0124-3 Source Proceedings of the 62nd IEEE Conference on Decision and Control, CDC 2023 Event 62nd IEEE Conference on Decision and Control, CDC 2023, 2023-12-13 → 2023-12-15, Singapore, Singapore Series Proceedings of the IEEE Conference on Decision and Control, 0743-1546 Bibliographical note Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public. Part of collection Institutional Repository Document type conference paper Rights © 2023 M. Guo, Claudio De Persis, Pietro Tesi Files file embargo until 2024-07-19