Fast Fixed-Time Distributed Neural Formation Control-Based Disturbance Observer for Multiple Quadrotor UAVs Under Unknown Disturbances

To enhance the robustness and adaptability of autonomous formation flight, this study develops a fast fixed-time distributed neural formation control-based disturbance observer for multiple quadrotor unmanned aerial vehicles with nonlinear dynamics, input saturation, and unknown disturbances. First, a fast fixed-time stable system is presented to enhance convergence speed. Building on this system, a distributed state observer is proposed to smoothly estimate the state of the leading quadrotor. Second, an adaptive fixed-time disturbance observer is proposed, which resolves chattering issues, enables rapid disturbance estimation, and updates the observer gain. To address the problem of input saturation, a modified anti-saturation method is developed via a Gaussian function and an auxiliary compensation system, achieving high saturation-approximation accuracy and mitigating the impact of approximation error. In addition, a low-computation adaptive neural law is proposed to handle nonlinear dynamics. This mechanism features fewer learning parameters that are independent of the network weight dimensions, thereby reducing computational demands. Theoretical analysis shows that the control errors converge to near the origin within a fixed time. Finally, comparative simulations are conducted to validate the effectiveness and superiority of the presented control strategy