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- W2808629054 abstract "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." @default.
- W2808629054 created "2018-06-21" @default.
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- W2808629054 date "2018-01-01" @default.
- W2808629054 modified "2023-09-24" @default.
- W2808629054 title "Neural networks-based robust adaptive flight path tracking control of large transport" @default.
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- W2808629054 doi "https://doi.org/10.30765/er.38.3.3" @default.
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