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arXiv · 2011.03716

Data-Driven Koopman Controller Synthesis Based on the Extended $\mathcal{H}_2$ Norm Characterization

Daisuke Uchida·Atsushi Yamashita·Hajime Asama
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Abstract

This paper presents a new data-driven controller synthesis based on the Koopman operator and the extended $\mathcal{H}_2$ norm characterization of discrete-time linear systems. We model dynamical systems as polytope sets which are derived from multiple data-driven linear models obtained by the finite approximation of the Koopman operator and then used to design robust feedback controllers combined with the $\mathcal{H}_2$ norm characterization. The use of the $\mathcal{H}_2$ norm characterization is aimed to deal with the model uncertainty that arises due to the nature of the data-driven setting of the problem. The effectiveness of the proposed controller synthesis is investigated through numerical simulations.

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Daisuke Uchida, Atsushi Yamashita, Hajime Asama. 2020-11-07. Data-Driven Koopman Controller Synthesis Based on the Extended $\mathcal{H}_2$ Norm Characterization. https://doi.org/10.1109/lcsys.2020.3042827

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