Reachability Analysis of Neural Network Control Systems With Tunable Accuracy and Efficiency
Yuhao Zhang, Hang Zhang, Xiangru Xu · IEEE Control Systems Letters · 2024
The surging popularity of neural networks in controlled systems underscores the imperative for formal verification to ensure the reliability and safety of such systems. Existing set propagation-based approaches for reachability analysis in neural network control systems encounter challenges in scalability and flexibility. This letter introduces a novel tunable hybrid zonotope-based method for computing both forward and backward reachable sets of neural network control systems. The proposed method incorporates an optimization-based network reduction technique and an activation pattern-based hybrid zonotope propagation approach for ReLU-activated feedforward neural networks. Furthermore, it enables two tunable parameters to balance computational complexity and approximation accuracy. A numerical example is provided to illustrate the performance and tunability of the proposed approach.