Data-Driven Control of Continuous-Time LTI Systems via Non-Minimal Realizations
Fuente:
arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
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| _version_ | 1866908382916509696 |
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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 article proposes an approach to design output-feedback controllers for unknown continuous-time linear time-invariant systems using only input-output data from a single experiment. To address the lack of state and derivative measurements, we introduce non-minimal realizations whose states can be observed by filtering the available data. We first apply this concept to the disturbance-free case, formulating linear matrix inequalities (LMIs) from batches of sampled signals to design a dynamic, filter-based stabilizing controller. The framework is then extended to the problem of asymptotic tracking and disturbance rejection - in short, output regulation - by incorporating an internal model based on prior knowledge of the disturbance/reference frequencies. Finally, we discuss tuning strategies for a class of multi-input multi-output systems and illustrate the method via numerical examples. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_22505 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Data-Driven Control of Continuous-Time LTI Systems via Non-Minimal Realizations Bosso, Alessandro Borghesi, Marco Iannelli, Andrea Notarstefano, Giuseppe Teel, Andrew R. Systems and Control This article proposes an approach to design output-feedback controllers for unknown continuous-time linear time-invariant systems using only input-output data from a single experiment. To address the lack of state and derivative measurements, we introduce non-minimal realizations whose states can be observed by filtering the available data. We first apply this concept to the disturbance-free case, formulating linear matrix inequalities (LMIs) from batches of sampled signals to design a dynamic, filter-based stabilizing controller. The framework is then extended to the problem of asymptotic tracking and disturbance rejection - in short, output regulation - by incorporating an internal model based on prior knowledge of the disturbance/reference frequencies. Finally, we discuss tuning strategies for a class of multi-input multi-output systems and illustrate the method via numerical examples. |
| title | Data-Driven Control of Continuous-Time LTI Systems via Non-Minimal Realizations |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2505.22505 |