Resilient Consensus Control of Nonlinear Multiagent Systems Under Hybrid Cyberattacks: A Disturbance Observer-Based Neural Network Approach

This article proposes a novel observer-based adaptive neural network-based resilient consensus control approach to address hybrid cyberattacks, disturbances, and nonlinear dynamics in nonlinear leader-following multiagent systems (MASs). Specifically, a dimension expansion methodology is developed to dynamically model and compensate for false data injection (FDI) attacks, while denial-of-service (DoS) attacks are probabilistically characterized via Bernoulli variables, forming a comprehensive hybrid attack mitigation strategy. Then, a cascaded observer is designed, integrating dimension-extended system modeling with disturbance decoupling to simultaneously estimate system states and external disturbances with high precision. Furthermore, an adaptive neural network-based approximation scheme is employed to handle system nonlinearities, eliminating the conservatism of Lipschitz-based methods while enhancing robustness in complex environments. Finally, the simulation result validates that the proposed control method achieves resilient consensus of leader-following MASs under hybrid cyberattacks, disturbances, and nonlinear dynamics.