Adaptive NN-Based output consensus control for High-Order nonlinear Multi-Agent systems with DoS attacks and disturbances

This study addresses the output-feedback consensus problem for high-order nonlinear multi-agent systems (MASs) affected by partial denial-of-service (DoS) attacks and external disturbances. To recover the state information of the agents, adaptive neural-network (NN)-based observers with compensation terms are developed, which improve both attack resilience and disturbance attenuation capability. With the aid of filtering techniques and the backstepping design framework, a distributed secure consensus controller is established to compensate for nonlinear dynamics, external disturbances, and adaptive estimation errors. Based on Lyapunov stability analysis, it is shown that the resulting consensus errors are uniformly ultimately bounded under the proposed adaptive NN-assisted observer and backstepping control signals. Comparative simulations are provided to demonstrate the feasibility and effectiveness of the developed resilient observation-based secure control strategy for a disturbed MAS.