{"ID":2823545,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2601.00476","arxiv_id":"2601.00476","title":"Safe Adaptive Feedback Control via Barrier States","abstract":"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.","short_abstract":"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...","url_abs":"https://arxiv.org/abs/2601.00476","url_pdf":"https://arxiv.org/pdf/2601.00476v1","authors":"[\"Trivikram Satharasi\",\"Tochukwu E. Ogri\",\"Muzaffar Qureshi\",\"Kyle Volle\",\"Rushikesh Kamalapurkar\"]","published":"2026-01-01T21:10:49Z","proceeding":"math.OC","tasks":"[\"math.OC\"]","methods":"[\"Reinforcement Learning\",\"Large Language Model\"]","has_code":false}