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DOE OSTI · 2293484

Deep Koopman learning of nonlinear time-varying systems

Hao, Wenjian·Huang, Bowen·Pan, Wei·Wu, Di·Mou, Shaoshuai
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Abstract

Here this paper presents a data-driven approach to approximate the dynamics of a nonlinear time-varying system (NTVS) by a linear time-varying system (LTVS), which results from the Koopman operator and deep neural networks. Analysis of the approximation error between states of the NTVS and the resulting LTVS is presented. Simulations on a representative NTVS show that the proposed method achieves small approximation errors, even when the system changes rapidly. Furthermore, simulations in an example of quadcopters demonstrate the computational efficiency of the proposed approach.

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97 MATHEMATICS AND COMPUTINGKoopman operatordeep neural networksnonlinear time-varying systems

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BibTeXRIS

Hao, Wenjian, Huang, Bowen, Pan, Wei, Wu, Di, Mou, Shaoshuai. 2023-10-21. Deep Koopman learning of nonlinear time-varying systems. https://doi.org/10.1016/j.automatica.2023.111372

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