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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2412.17501 |
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| _version_ | 1866910759941832704 |
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| author | Mandal, Swarnendu Chauhan, Swati Verma, Umesh Kumar Shrimali, Manish Dev Aihara, Kazuyuki |
| author_facet | Mandal, Swarnendu Chauhan, Swati Verma, Umesh Kumar Shrimali, Manish Dev Aihara, Kazuyuki |
| contents | We demonstrate a data-driven technique for adaptive control in dynamical systems that exploits the reservoir computing method. We show that a reservoir computer can be trained to predict a system parameter from the time series data. Subsequently, a control signal based on the predicted parameter can be used as feedback to the dynamical system to lead it to a target state. Our results show that the dynamical system can be controlled throughout a wide range of attractor types. One set of training data consisting of only a few time series corresponding to the known parameter values enables our scheme to control a dynamical system to an arbitrary target attractor starting from any other initial attractor. In addition to numerical results, we implement our scheme in real-world systems like on a Rössler system realized in an electronic circuit to demonstrate the effectiveness of our approach. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_17501 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Adaptive control in dynamical systems using reservoir computing Mandal, Swarnendu Chauhan, Swati Verma, Umesh Kumar Shrimali, Manish Dev Aihara, Kazuyuki Chaotic Dynamics We demonstrate a data-driven technique for adaptive control in dynamical systems that exploits the reservoir computing method. We show that a reservoir computer can be trained to predict a system parameter from the time series data. Subsequently, a control signal based on the predicted parameter can be used as feedback to the dynamical system to lead it to a target state. Our results show that the dynamical system can be controlled throughout a wide range of attractor types. One set of training data consisting of only a few time series corresponding to the known parameter values enables our scheme to control a dynamical system to an arbitrary target attractor starting from any other initial attractor. In addition to numerical results, we implement our scheme in real-world systems like on a Rössler system realized in an electronic circuit to demonstrate the effectiveness of our approach. |
| title | Adaptive control in dynamical systems using reservoir computing |
| topic | Chaotic Dynamics |
| url | https://arxiv.org/abs/2412.17501 |