Neural networks-based robust adaptive flight path tracking control of large transport
Keyword(s):
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.
2015 ◽
Vol 26
(11)
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pp. 2844-2860
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2021 ◽
pp. 014233122110195
2016 ◽
Vol 13
(4)
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pp. 409-416
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2012 ◽
Vol 9
(2)
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pp. 135-141
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2020 ◽
Vol 56
(2)
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pp. 1406-1419
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