This paper presents a high-precision tracking control scheme for quadrotor unmanned aerial vehicles (UAVs) in a time-varying time-delay and disturbance environment. Firstly, the dynamic model of the quadrotor unmanned aerial vehicle is decoupled into the attitude loop and the position loop. For the attitude loop, the adaptive disturbance rejection control is improved by using neural networks. The neural network is used to dynamically compensate for the observation deviation of the extended state observer and to achieve online identification of the dynamic parameters of the neural network. This not only enhances the observation accuracy of the attitude loop but also reduces the dependence of the control system on the precise model. For the position loop, a decoupled dual observer compensation scheme combined with the heterogeneous axial control architecture is proposed. The dynamic surface sliding mode control using the combined hyperbolic function maintains the strong robustness of the horizontal axis while achieving rapid convergence of the vertical axis through PD control. The improved time-varying delay observer and disturbance observer work through the decoupling structure, effectively suppressing the observation error amplification problem caused by the combined GPS delay and external interference, and enhances the system robustness. Based on Lyapunov theory, the stability of the control scheme is proved separately. Through ablation experiments and comparisons with other control methods, the simulation results verify the superiority and effectiveness of the proposed control scheme.
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