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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2605.16752 |
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| _version_ | 1866914571721113600 |
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| author | Li, Weijian Yi, Bowen Antsaklis, Panos J. Lin, Hai |
| author_facet | Li, Weijian Yi, Bowen Antsaklis, Panos J. Lin, Hai |
| contents | In this paper, we investigate a continuous-time linear quadratic control problem for systems with unknown matrices, where only input-output data are available. We propose an output-feedback learning framework based on a canonical nonminimal realization constructed through Kreisselmeier's adaptive filter. The filter admits an observer interpretation, which leads to an augmented system that preserves the input-output response of the realization and provides accessible state trajectories. We show that the optimal gain of this augmented system explicitly recovers the optimal gain associated with the canonical non-minimal realization, and hence achieves the optimal state-feedback solution of the original plant. Exploiting this relation and the known structure of the augmented input matrix, we develop a data-driven value iteration algorithm within the adaptive dynamic programming framework. The resulting controller is implementable from input-output data, and its performance is validated via simulations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_16752 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Data-Driven Linear Quadratic Control Using Output-Feedback via Non-Minimal Realization Li, Weijian Yi, Bowen Antsaklis, Panos J. Lin, Hai Optimization and Control In this paper, we investigate a continuous-time linear quadratic control problem for systems with unknown matrices, where only input-output data are available. We propose an output-feedback learning framework based on a canonical nonminimal realization constructed through Kreisselmeier's adaptive filter. The filter admits an observer interpretation, which leads to an augmented system that preserves the input-output response of the realization and provides accessible state trajectories. We show that the optimal gain of this augmented system explicitly recovers the optimal gain associated with the canonical non-minimal realization, and hence achieves the optimal state-feedback solution of the original plant. Exploiting this relation and the known structure of the augmented input matrix, we develop a data-driven value iteration algorithm within the adaptive dynamic programming framework. The resulting controller is implementable from input-output data, and its performance is validated via simulations. |
| title | Data-Driven Linear Quadratic Control Using Output-Feedback via Non-Minimal Realization |
| topic | Optimization and Control |
| url | https://arxiv.org/abs/2605.16752 |