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

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.

arXiv comment6 pages, 6 figures

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Cited by in corpus (2)

• System Norm Regularization Methods for Koopman Operator Approximation
• Closed-Loop Koopman Operator Approximation