Delay Independent Safe Control with Neural Networks: Positive Lur'e Certificates for Risk Aware Autonomy

• URL: http://arxiv.org/abs/2510.06661v1
• Date: Wed, 08 Oct 2025 05:22:28 GMT
• Title: Delay Independent Safe Control with Neural Networks: Positive Lur'e Certificates for Risk Aware Autonomy
• Authors: Hamidreza Montazeri Hedesh, Milad Siami,
• Abstract summary: We present a risk-aware safety certification method for autonomous, learning enabled control systems.<n>We model the neural network (NN) controller with local sector bounds and exploit positivity structure to derive linear, delay-independent certificates.
• Score: 0.5729426778193398
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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