Derivative-Free Data-Driven Control of Continuous-Time Linear Time-Invariant Systems
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arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866916463487483904 |
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| author | Bosso, Alessandro Borghesi, Marco Iannelli, Andrea Notarstefano, Giuseppe Teel, Andrew R. |
| author_facet | Bosso, Alessandro Borghesi, Marco Iannelli, Andrea Notarstefano, Giuseppe Teel, Andrew R. |
| contents | This paper develops a data-driven stabilization method for continuous-time linear time-invariant systems with theoretical guarantees and no need for signal derivatives. The framework, based on linear matrix inequalities (LMIs), is illustrated in the state-feedback and single-input single-output output-feedback scenarios. Similar to discrete-time approaches, we rely solely on input and state/output measurements. To avoid differentiation, we employ low-pass filters of the available signals that, rather than approximating the derivatives, reconstruct a non-minimal realization of the plant. With access to the filter states and their derivatives, we can solve LMIs derived from sample batches of the available signals to compute a dynamic controller that stabilizes the plant. The effectiveness of the framework is showcased through numerical examples. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_24167 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Derivative-Free Data-Driven Control of Continuous-Time Linear Time-Invariant Systems Bosso, Alessandro Borghesi, Marco Iannelli, Andrea Notarstefano, Giuseppe Teel, Andrew R. Optimization and Control Systems and Control This paper develops a data-driven stabilization method for continuous-time linear time-invariant systems with theoretical guarantees and no need for signal derivatives. The framework, based on linear matrix inequalities (LMIs), is illustrated in the state-feedback and single-input single-output output-feedback scenarios. Similar to discrete-time approaches, we rely solely on input and state/output measurements. To avoid differentiation, we employ low-pass filters of the available signals that, rather than approximating the derivatives, reconstruct a non-minimal realization of the plant. With access to the filter states and their derivatives, we can solve LMIs derived from sample batches of the available signals to compute a dynamic controller that stabilizes the plant. The effectiveness of the framework is showcased through numerical examples. |
| title | Derivative-Free Data-Driven Control of Continuous-Time Linear Time-Invariant Systems |
| topic | Optimization and Control Systems and Control |
| url | https://arxiv.org/abs/2410.24167 |