mlsanityData to 7 Oct 2026

Learning Safe Neural Network Controllers with Barrier Certificates

arXiv 2009.09826 ↗First posted 18 Sep 2020eess.SY cs.AI cs.LGcited by 16

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Abstract (arXiv)

We provide a novel approach to synthesize controllers for nonlinear continuous dynamical systems with control against safety properties. The controllers are based on neural networks (NNs). To certify the safety property we utilize barrier functions, which are represented by NNs as well. We train the controller-NN and barrier-NN simultaneously, achieving a verification-in-the-loop synthesis. We provide a prototype tool nncontroller with a number of case studies. The experiment results confirm the feasibility and efficacy of our approach.

Key facts. Cited by 16 papers in the map (data through 7 Oct 2026). First posted on arXiv 18 Sep 2020.

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Citing papers per year: 2021: 3; 2022: 3; 2023: 3; 2024: 4; 2025: 3.

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