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

Authors

  • Lv Maolong School of Aeronautics and Astronautics Engineering, Air Force Engineering University, Xi'an baling road1,710038, China
  • Xiuxia Sun School of Aeronautics and Astronautics Engineering, Air Force Engineering University, Xi'an baling road1,710038, China
  • G. Z. Xu Chinese people’s Liberation Army
  • Z. T. Wang Chinese people’s Liberation Army

Keywords:

ultra-low altitude airdrop, actuator input nonlinearity, neural network, adaptive control, dynamic surface control, flight path angle

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.

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Published

2018-06-11

Issue

Engineering Review, Vol. 38, No. 3

Section

Articles

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