Neural-network-based practical specified-time resilient formation maneuver control for second-order nonlinear multi-robot systems under FDI attacks

This paper presents a specified-time resilient formation maneuver control approach for second-order nonlinear multi-robot systems under false data injection (FDI) attacks, incorporating an offline neural network. Building on existing works in integrated distributed localization and specified-time formation maneuver, the proposed approach introduces a hierarchical topology framework based on $(d+1)$-reachability theory to achieve downward decoupling, ensuring that each robot in a given layer remains unaffected by attacks on lower-layer robots. The framework enhances resilience by restricting the flow of follower information to the current and previous layers and the leader, thereby improving distributed relative localization accuracy. An offline radial basis function neural network (RBFNN) is employed to mitigate unknown nonlinearities and FDI attacks, enabling the control protocol to achieve specified time convergence while reducing system errors compared to traditional finite-time and fixed-time methods. Simulation results validate the effectiveness of the method with enhanced robustness and reduced error under adversarial conditions.