Learning controllers from data via kernel-based interpolation
Systems and Control
2023-04-20 v1 Systems and Control
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.
Cite
@article{arxiv.2304.09577,
title = {Learning controllers from data via kernel-based interpolation},
author = {Zhongjie Hu and Claudio De Persis and Pietro Tesi},
journal= {arXiv preprint arXiv:2304.09577},
year = {2023}
}