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arXiv · 2304.09577

Learning controllers from data via kernel-based interpolation

Zhongjie Hu·Claudio De Persis·Pietro Tesi
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Abstract

We propose a data-driven control design method for nonlinear systems that builds on kernel-based interpolation. Under some assumptions on the system dynamics, kernel-based functions are built from data and a model of the system, along with deterministic model error bounds, is determined. Then, we derive a controller design method that aims at stabilizing the closed-loop system by cancelling out the system nonlinearities. The proposed method can be implemented using semidefinite programming and returns positively invariant sets for the closed-loop system.

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Zhongjie Hu, Claudio De Persis, Pietro Tesi. 2023-04-19. Learning controllers from data via kernel-based interpolation. https://arxiv.org/abs/2304.09577

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