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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  neural network is used to approximate unknown nonlinear continuous  functions of the 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 rigorously 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.

Neural networks-based robust adaptive flight path tracking control of large transport