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arXiv · 2309.09208

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

Xiaoyan Dai·Claudio De Persis·Nima Monshizadeh·Pietro Tesi
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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.

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Xiaoyan Dai, Claudio De Persis, Nima Monshizadeh, Pietro Tesi. 2023-09-17. Data-driven control of nonlinear systems from input-output data. https://doi.org/10.1109/cdc49753.2023.10384071

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