Safe Adaptive Feedback Control via Barrier States
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866909979880980480 |
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| author | Satharasi, Trivikram Ogri, Tochukwu E. Qureshi, Muzaffar Volle, Kyle Kamalapurkar, Rushikesh |
| author_facet | Satharasi, Trivikram Ogri, Tochukwu E. Qureshi, Muzaffar Volle, Kyle Kamalapurkar, Rushikesh |
| contents | This paper presents a safe feedback control framework for nonlinear control-affine systems with parametric uncertainty by leveraging adaptive dynamic programming (ADP) with barrier-state augmentation. The developed ADP-based controller enforces control invariance by optimizing a value function that explicitly penalizes the barrier state, thereby embedding safety directly into the Bellman structure. The near-optimal control policy computed using model-based reinforcement learning is combined with a concurrent learning estimator to identify the unknown parameters and guarantee uniform convergence without requiring persistency of excitation. Using a barrier-state Lyapunov function, we establish boundedness of the barrier dynamics and prove closed-loop stability and safety. Numerical simulations on an optimal obstacle-avoidance problem validate the effectiveness of the developed approach. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_00476 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Safe Adaptive Feedback Control via Barrier States Satharasi, Trivikram Ogri, Tochukwu E. Qureshi, Muzaffar Volle, Kyle Kamalapurkar, Rushikesh Optimization and Control This paper presents a safe feedback control framework for nonlinear control-affine systems with parametric uncertainty by leveraging adaptive dynamic programming (ADP) with barrier-state augmentation. The developed ADP-based controller enforces control invariance by optimizing a value function that explicitly penalizes the barrier state, thereby embedding safety directly into the Bellman structure. The near-optimal control policy computed using model-based reinforcement learning is combined with a concurrent learning estimator to identify the unknown parameters and guarantee uniform convergence without requiring persistency of excitation. Using a barrier-state Lyapunov function, we establish boundedness of the barrier dynamics and prove closed-loop stability and safety. Numerical simulations on an optimal obstacle-avoidance problem validate the effectiveness of the developed approach. |
| title | Safe Adaptive Feedback Control via Barrier States |
| topic | Optimization and Control |
| url | https://arxiv.org/abs/2601.00476 |