Safety Verification of Neural Feedback Systems Based on Constrained Zonotopes
- Resource Type
- Conference
- Authors
- Zhang, Yuhao; Xu, Xiangru
- Source
- 2022 IEEE 61st Conference on Decision and Control (CDC) Decision and Control (CDC), 2022 IEEE 61st Conference on. :2737-2744 Dec, 2022
- Subject
- Robotics and Control Systems
Sufficient conditions
Computational modeling
Safety
Numerical models
Computational efficiency
Trajectory
Feedforward neural networks
- Language
- ISSN
- 2576-2370
Artificial neural networks have recently been utilized in many feedback control systems and introduced new challenges regarding the safety of such systems. This paper considers the safe verification problem for a dynamical system with a given feedforward neural network as the feedback controller by using a constrained zonotope-based approach. A novel set-based method is proposed to compute both exact and over-approximated reachable sets for neural feedback systems with linear models, and linear program-based sufficient conditions are presented to verify whether the trajectories of such a system can avoid unsafe regions represented as constrained zonotopes. The results are also extended to neural feedback systems with nonlinear models. The computational efficiency and accuracy of the proposed method are demonstrated by two numerical examples where a comparison with state-of-the-art methods is also provided.