论文标题
通过屏障证书学习安全的神经网络控制器
Learning Safe Neural Network Controllers with Barrier Certificates
论文作者
论文摘要
我们为非线性连续动力系统合成控制器提供了一种新颖的方法，并控制了针对安全性能的控制器。控制器基于神经网络（NNS）。为了证明我们利用屏障功能的安全性，也由NNS代表。我们同时训练控制器-NN和障碍-NN，实现了环境验证。我们提供了许多案例研究的原型工具NNController。实验结果证实了我们方法的可行性和功效。
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.
