Distributed robust data-driven event-triggered control for QUAVs under stochastic disturbances

To address the issue of instability or even imbalance in the orientation and attitude control of quadrotor unmanned aerial vehicles (QUAVs) under random disturbances, this paper proposes a distributed anti-disturbance data-driven event-triggered fusion control method, which achieves efficient fault diagnosis while suppressing random disturbances and mitigating communication conflicts within the QUAV swarm. First, the impact of random disturbances on the UAV swarm is analyzed, and a model for orientation and attitude control of QUAVs under stochastic perturbations is established, with the disturbance gain threshold determined. Second, a fault diagnosis system based on a high-gain observer is designed, constructing a fault gain criterion by integrating orientation and attitude information from QUAVs. Subsequently, a model-free dynamic linearization-based data modeling (MFDLDM) framework is developed using model-free adaptive control, which efficiently fits the nonlinear control model of the QUAV swarm while reducing temporal constraints on control data. On this basis, this paper constructs a distributed data-driven event-triggered controller based on the staggered communication mechanism, which consists of an equivalent QUAV controller and an event-triggered controller, and is able to reduce the communication conflicts while suppressing the influence of random interference. Finally, by incorporating random disturbances into the controller, comparative experiments and physical validations are conducted on the QUAV platforms, fully demonstrating the strong adaptability and robustness of the proposed distributed event-triggered fault-tolerant control system.