Data-Driven Output Regulation via Internal Model Principle
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arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866929500273508352 |
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| author | Lin, Liquan Huang, Jie |
| author_facet | Lin, Liquan Huang, Jie |
| contents | The data-driven techniques have been developed to deal with the output regulation problem of unknown linear systems by various approaches. In this paper, we first extend an existing algorithm from single-input single-output linear systems to multi-input multi-output linear systems. Then, by separating the dynamics used in the learning phase and the control phase, we further propose an improved algorithm that significantly reduces the computational cost and weakens the solvability conditions over the first algorithm. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_09571 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Data-Driven Output Regulation via Internal Model Principle Lin, Liquan Huang, Jie Optimization and Control The data-driven techniques have been developed to deal with the output regulation problem of unknown linear systems by various approaches. In this paper, we first extend an existing algorithm from single-input single-output linear systems to multi-input multi-output linear systems. Then, by separating the dynamics used in the learning phase and the control phase, we further propose an improved algorithm that significantly reduces the computational cost and weakens the solvability conditions over the first algorithm. |
| title | Data-Driven Output Regulation via Internal Model Principle |
| topic | Optimization and Control |
| url | https://arxiv.org/abs/2409.09571 |