An adaptive DSC for ultra-low altitude airdrop path tracking with actuator input dead zone

For the ultralow altitude airdrop decline stage, many factors such as actuator nonlinearity, the uncertain atmospheric disturbances and model unknown nonlinearity affect the precision of trajectory tracking, A robust adaptive neural network dynamic surface control method is proposed. The ultra-low altitude airdrop longitudinal dynamics with actuator input nonlinearity is established, the neural network is used to approximate unknown nonlinear functions of model and a nonlinear robust term is introduced to eliminate the actuator's nonlinear modeling error and external disturbances. From Lyapunov stability theorem, it is proved that all the signals in the closed-loop system are bounded. Simulation results confirm the perfect tracking performance and strong robustness of the proposed method.