This paper investigates a formation control strategy for multiple quadrotor unmanned aerial vehicles (QUAVs). The formation communication topology is first modeled using graph theory, and the dynamics of an individual QUAV are derived based on the Newton–Euler formulation. A multi-QUAV formation model is then established within a leader–follower framework. To address the dual challenges of lumped internal disturbances and time-varying environmental perturbations, a control scheme integrating fast non-singular terminal sliding mode control (FNTSMC) with a radial basis function neural network (RBFNN) is proposed. The RBFNN compensates for system uncertainties in real time, while a novel continuous sliding surface is designed to ensure finite-time convergence and reduce chattering, thereby enhancing formation responsiveness. The stability of the closed-loop system is rigorously demonstrated through Lyapunov analysis. Numerical simulations verify that the proposed control strategy reduces the root mean square error (RMSE) of the overall formation tracking error by more than 90%, with the disturbance estimation error being less than 5%. It can achieve high-precision formation control under both mixed wind and rectangular wind disturbances.
Zhu et al. (Thu,) studied this question.