Adaptive neural networks formation secure control for nonlinear quadrotor UAV systems under wind disturbances and DoS attacks

This paper addresses a cooperative control challenge of disturbance rejection and cybersecurity for quadrotor UAV formations in low-altitude environments when confronted with composite wind disturbances and asynchronous multi-channel denial-of-service (DoS) attacks. This paper innovatively proposes an adaptive neural network formation safety controller with a Linear Extended State Observer (LESO) for an uncertain nonlinear quadrotor UAV Systems, thereby solving the formation consensus problem of multiple UAVs under the dual threat of DoS attacks and wind disturbances. The proposed controller guarantees that all closed-loop signals remain semiglobally uniformly ultimately bounded (SGUUB), while the error signals converges to an adjustable neighbourhood of zero. Finally, the effectiveness of the proposed method is demonstrated through simulation using a rhombus formation while tracking an 8-pattern trajectory.