This paper investigates an observer-based secure control method for networked non-Lipschitz nonlinear systems subject to unknown nonlinearities, external disturbances, sensor noises, and intermittent denial-of-service (DoS) attacks. Multi-layer neural networks (MNNs) are adopted to compensate for non-smooth, non-Lipschitz terms, guaranteeing bounded approximation errors. A resilient high-gain observer fused with the MNN is developed to continuously reconstruct system states. When DoS attacks block sensor channels, the observer acts as a virtual dynamic engine to substitute for lost real-time measurements, providing uninterrupted feedback to the controller. Furthermore, to optimize communication efficiency, an observer-based static event-triggered mechanism (SETM) coupled with a hold-input strategy is integrated. Employing the Lyapunov–Krasovskii functional method, sufficient conditions are derived to prove that the closed-loop system remains uniformly ultimately bounded (UUB) under the joint effects of approximation errors, disturbances, and attacks. Simulation results on a two-link manipulator demonstrate that the proposed secure control scheme effectively counters aggressive DoS attacks while achieving a 56.8% reduction in network transmissions compared with conventional periodic sampling paradigms, striking a favorable balance between tracking accuracy and resource efficiency.
Lu et al. (Fri,) studied this question.