Learning Safe Neural Network Controllers with Barrier Certificates

Hengjun Zhao, Xia Zeng, Taolue Chen, Zhiming Liu, Jim Woodcock

Research output: Working paperPreprint


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.
Original languageEnglish
Publication statusPublished - 18 Sep 2020

Publication series



  • Continuous dynamical systems
  • Controller synthesis
  • Neural networks
  • Safety verification
  • Barrier certificates

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