Suppressing unknown disturbances to dynamical systems using machine learning
Fuente:
arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2023
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| _version_ | 1866911996836839424 |
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| author | Restrepo, Juan G. Byers, Clayton P. Skardal, Per Sebastian |
| author_facet | Restrepo, Juan G. Byers, Clayton P. Skardal, Per Sebastian |
| contents | Identifying and suppressing unknown disturbances to dynamical systems is a problem with applications in many different fields. Here we present a model-free method to identify and suppress an unknown disturbance to an unknown system based only on previous observations of the system under the influence of a known forcing function. We find that, under very mild restrictions on the training function, our method is able to robustly identify and suppress a large class of unknown disturbances. We illustrate our scheme with the identification of both deterministic and stochastic unknown disturbances to an analog electric chaotic circuit and with numerical examples where a chaotic disturbance to various chaotic dynamical systems is identified and suppressed. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2307_03690 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Suppressing unknown disturbances to dynamical systems using machine learning Restrepo, Juan G. Byers, Clayton P. Skardal, Per Sebastian Systems and Control Machine Learning Identifying and suppressing unknown disturbances to dynamical systems is a problem with applications in many different fields. Here we present a model-free method to identify and suppress an unknown disturbance to an unknown system based only on previous observations of the system under the influence of a known forcing function. We find that, under very mild restrictions on the training function, our method is able to robustly identify and suppress a large class of unknown disturbances. We illustrate our scheme with the identification of both deterministic and stochastic unknown disturbances to an analog electric chaotic circuit and with numerical examples where a chaotic disturbance to various chaotic dynamical systems is identified and suppressed. |
| title | Suppressing unknown disturbances to dynamical systems using machine learning |
| topic | Systems and Control Machine Learning |
| url | https://arxiv.org/abs/2307.03690 |