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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2401.10785 |
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| _version_ | 1866917414460981248 |
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| author | Shi, Tian Li, Shihua Wen, Changyun Pan, Yongping |
| author_facet | Shi, Tian Li, Shihua Wen, Changyun Pan, Yongping |
| contents | This paper proposes a composite learning backstepping control (CLBC) strategy based on modular backstepping and high-order tuners to achieve closed-loop exponential stability without high-gain feedback and PE. A novel composite learning mechanism that maximizes the staged exciting strength is designed for parameter estimation, enabling parameter convergence under interval excitation (IE) or even partial IE, which is strictly weaker than PE. An extra prediction error is employed in the adaptive law to ensure the transient performance without high-gain feedback. Simulations have demonstrated the effectiveness and superiority of the proposed method in both parameter estimation and control compared to state-of-the-art methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_10785 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Composite learning control with modular backstepping and high-order tuners Shi, Tian Li, Shihua Wen, Changyun Pan, Yongping Systems and Control This paper proposes a composite learning backstepping control (CLBC) strategy based on modular backstepping and high-order tuners to achieve closed-loop exponential stability without high-gain feedback and PE. A novel composite learning mechanism that maximizes the staged exciting strength is designed for parameter estimation, enabling parameter convergence under interval excitation (IE) or even partial IE, which is strictly weaker than PE. An extra prediction error is employed in the adaptive law to ensure the transient performance without high-gain feedback. Simulations have demonstrated the effectiveness and superiority of the proposed method in both parameter estimation and control compared to state-of-the-art methods. |
| title | Composite learning control with modular backstepping and high-order tuners |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2401.10785 |