Deep Koopman Learning of Nonlinear Time-Varying Systems
Fuente:
arXiv
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| Hauptverfasser: | , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2022
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| _version_ | 1866915857848860672 |
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| author | Hao, Wenjian Huang, Bowen Pan, Wei Wu, Di Mou, Shaoshuai |
| author_facet | Hao, Wenjian Huang, Bowen Pan, Wei Wu, Di Mou, Shaoshuai |
| contents | 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 is resulted 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2210_06272 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Deep Koopman Learning of Nonlinear Time-Varying Systems Hao, Wenjian Huang, Bowen Pan, Wei Wu, Di Mou, Shaoshuai Systems and Control 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 is resulted 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. |
| title | Deep Koopman Learning of Nonlinear Time-Varying Systems |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2210.06272 |