Data-Driven Control of Nonlinear Systems from Input-Output Data

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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.
Original languageEnglish
Title of host publicationProceedings of the 62nd IEEE Conference on Decision and Control (CDC 2023)
Number of pages6
ISBN (Electronic)979-8-3503-0124-3
Publication statusPublished - 19-Jan-2024
Event62nd IEEE Conference on Decision and Control - Marina Bay Sands, Singapore, Singapore
Duration: 13-Dec-202315-Dec-2023


Conference62nd IEEE Conference on Decision and Control
Abbreviated titleCDC 2023
Internet address


  • Data driven control, Nonlinear systems

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