swMATH ID: 39887
Software Authors: Zhao, Hengjun; Zeng, Xia; Chen, Taolue; Liu, Zhiming; Woodcock, Jim
Description: Learning safe neural network controllers with barrier certificates. We provide a new 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.
Homepage: https://link.springer.com/article/10.1007%2Fs00165-021-00544-5
Keywords: continuous dynamical systems; controller synthesis; neural networks; safety verification; barrier certificates
Related Software: Reluplex; Spacer; CLN2INV; AI2; Adam; Sherlock; ReachNN; GitHub; PENBMI; Safety Gym; VERIFAI; NNV; HSolver; Benchmarks; RSOLVER
Cited in: 4 Publications

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