Adaptive neural network based on fixed-time command-filtered control for quadrotor unmanned aerial vehicles

For the quadrotor unmanned aerial vehicle (QUAV) attitude tracking problem under external disturbance and model uncertainty, a fixed-time command-filtered control approach is developed based on the composite adaptive radial basis function (RBF) neural network. Firstly, a fixed-time command filter based on the hyperbolic tangent function is proposed, which avoids the differential explosion problem during the derivation of virtual control and eliminates the singularity phenomena of traditional command filters with fractional order effectively. Secondly, the online approximation impact is enhanced by using a RBF neural network to approximate the model uncertainty and designing the adaptive adjustment law of neural network weights based on the tracking deviation. Additionally, combined with the backstepping method and disturbance observer, a fixed-time control strategy for the QUAV system is established, and the external disturbance is estimated and compensated by the disturbance observer, enabling rapid and accurate tracking of desired attitudes. The stability of the proposed control strategy is rigorously proved via Lyapunov theory. Finally, the effectiveness of the control strategy is verified by numerical simulation.