Fixed-Time Quadrotors Formation Control Via Dynamic Surface Control with Disturbance Observer and Neural Networks

This paper investigates the formation control problem for multiple quadrotor systems subject to external disturbances, nonlinear unmodeled dynamics and input saturation constraints. A novel fixed-time anti-saturation control strategy is developed by integrating dynamic surface control with disturbance observation and neural network approximation. Specifically, an asymmetric saturation nonlinearity compensator is constructed to mitigate nonlinear effects induced by input constraints. A fixed-time sliding mode disturbance observer is designed for precise estimation of external disturbances. Furthermore, a novel fixed-time anti-saturation technique based on dynamic surface control and neural networks (NN) is proposed to provide fixed-time formation control for the quadrotor. The uniform ultimate boundedness of the closed-loop system is proven using Lyapunov theory. Finally, the effectiveness of the proposed method is demonstrated through simulation results.