Learning Safe Neural Network Controllers with Barrier Certificates
2020-09-18Code Available0· sign in to hype
Hengjun Zhao, Xia Zeng, Taolue Chen, Zhiming Liu, Jim Woodcock
Code Available — Be the first to reproduce this paper.
ReproduceCode
- github.com/zhaohj2017/FAoC-toolOfficialIn paperpytorch★ 3
Abstract
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.