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• W3129155484 abstract "Sparked by the Willems' fundamental lemma, a class of data-driven control methods has been developed for LTI systems. At the same time, the Koopman operator theory attempts to cast a nonlinear control problem into a standard linear one albeit infinite-dimensional. Motivated by these two ideas, a data-driven control scheme for nonlinear systems is proposed in this work. The proposed scheme is compatible with most differential regressors enabling offline learning. In particular, the model uncertainty is considered, enabling a novel data-driven simulation framework based on Wasserstein distance. Numerical experiments are performed with Bayesian neural networks to show the effectiveness of both the proposed control and simulation scheme." @default.
• W3129155484 created "2021-02-15" @default.
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• W3129155484 date "2021-02-09" @default.
• W3129155484 modified "2023-10-01" @default.
• W3129155484 title "Koopman based data-driven predictive control" @default.
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• W3129155484 hasPublicationYear "2021" @default.
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