Learning Safe Neural Network Controllers with Barrier Certificates
This addresses safety-critical control synthesis for nonlinear systems, though it appears incremental as it builds on existing barrier certificate methods with neural network integration.
The paper tackles the problem of synthesizing safe neural network controllers for nonlinear continuous dynamical systems by simultaneously training controller and barrier neural networks, achieving verification-in-the-loop synthesis with a prototype tool and confirming feasibility and efficacy in case studies.
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.