Data-Driven Adaptive Output Regulation of Unknown Linear Systems
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866917127366115328 |
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| author | Liu, Shangkun Wang, Lei Yi, Bowen |
| author_facet | Liu, Shangkun Wang, Lei Yi, Bowen |
| contents | This paper investigates the linear output regulation problem with both the exosystem and the plant fully unknown. A data-driven regulator is proposed to achieve asymptotic regulation and closed-loop stability without performing model identification. The method constructs a nominal approximate internal model and filters of input and outputs, thereby yielding a stabilizable cascaded nominal system whose states are available. For this nominal system, a stabilizing law is derived from an offline dataset that has been acquired from the plant during experiments, such that the system states exponentially converge to a subspace. An identifier in discrete-time is, then, implemented to correct the internal model and update the stabilizing law; as a result, the regulation error can be steered to zero asymptotically under some persistent excitation conditions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_05390 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Data-Driven Adaptive Output Regulation of Unknown Linear Systems Liu, Shangkun Wang, Lei Yi, Bowen Systems and Control This paper investigates the linear output regulation problem with both the exosystem and the plant fully unknown. A data-driven regulator is proposed to achieve asymptotic regulation and closed-loop stability without performing model identification. The method constructs a nominal approximate internal model and filters of input and outputs, thereby yielding a stabilizable cascaded nominal system whose states are available. For this nominal system, a stabilizing law is derived from an offline dataset that has been acquired from the plant during experiments, such that the system states exponentially converge to a subspace. An identifier in discrete-time is, then, implemented to correct the internal model and update the stabilizing law; as a result, the regulation error can be steered to zero asymptotically under some persistent excitation conditions. |
| title | Data-Driven Adaptive Output Regulation of Unknown Linear Systems |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2512.05390 |