{"ID":2858576,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.06661","arxiv_id":"2510.06661","title":"Delay Independent Safe Control with Neural Networks: Positive Lur'e Certificates for Risk Aware Autonomy","abstract":"We present a risk-aware safety certification method for autonomous, learning enabled control systems. Focusing on two realistic risks, state/input delays and interval matrix uncertainty, we model the neural network (NN) controller with local sector bounds and exploit positivity structure to derive linear, delay-independent certificates that guarantee local exponential stability across admissible uncertainties. To benchmark performance, we adopt and implement a state-of-the-art IQC NN verification pipeline. On representative cases, our positivity-based tests run orders of magnitude faster than SDP-based IQC while certifying regimes the latter cannot-providing scalable safety guarantees that complement risk-aware control.","short_abstract":"We present a risk-aware safety certification method for autonomous, learning enabled control systems. Focusing on two realistic risks, state/input delays and interval matrix uncertainty, we model the neural network (NN) controller with local sector bounds and exploit positivity structure to derive linear, delay-indepen...","url_abs":"https://arxiv.org/abs/2510.06661","url_pdf":"https://arxiv.org/pdf/2510.06661v1","authors":"[\"Hamidreza Montazeri Hedesh\",\"Milad Siami\"]","published":"2025-10-08T05:22:28Z","proceeding":"eess.SY","tasks":"[\"eess.SY\",\"cs.AI\"]","methods":"[]","has_code":false}