Safe Output-Feedback Adaptive Optimal Control of Affine Nonlinear Systems
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866915571515260928 |
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| author | Ogri, Tochukwu E. Qureshi, Muzaffar Bell, Zachary I. Makumi, Wanjiku A. Kamalapurkar, Rushikesh |
| author_facet | Ogri, Tochukwu E. Qureshi, Muzaffar Bell, Zachary I. Makumi, Wanjiku A. Kamalapurkar, Rushikesh |
| contents | In this paper, we develop a safe control synthesis method that integrates state estimation and parameter estimation within an adaptive optimal control (AOC) and control barrier function (CBF)-based control architecture. The developed approach decouples safety objectives from the learning objectives using a CBF-based guarding controller where the CBFs are robustified to account for the lack of full-state measurements. The coupling of this guarding controller with the AOC-based stabilizing control guarantees safety and regulation despite the lack of full state measurement. The paper leverages recent advancements in deep neural network-based adaptive observers to ensure safety in the presence of state estimation errors. Safety and convergence guarantees are provided using a Lyapunov-based analysis, and the effectiveness of the developed controller is demonstrated through simulation under mild excitation conditions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_20081 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Safe Output-Feedback Adaptive Optimal Control of Affine Nonlinear Systems Ogri, Tochukwu E. Qureshi, Muzaffar Bell, Zachary I. Makumi, Wanjiku A. Kamalapurkar, Rushikesh Systems and Control In this paper, we develop a safe control synthesis method that integrates state estimation and parameter estimation within an adaptive optimal control (AOC) and control barrier function (CBF)-based control architecture. The developed approach decouples safety objectives from the learning objectives using a CBF-based guarding controller where the CBFs are robustified to account for the lack of full-state measurements. The coupling of this guarding controller with the AOC-based stabilizing control guarantees safety and regulation despite the lack of full state measurement. The paper leverages recent advancements in deep neural network-based adaptive observers to ensure safety in the presence of state estimation errors. Safety and convergence guarantees are provided using a Lyapunov-based analysis, and the effectiveness of the developed controller is demonstrated through simulation under mild excitation conditions. |
| title | Safe Output-Feedback Adaptive Optimal Control of Affine Nonlinear Systems |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2510.20081 |