Application of Neural Network Dynamic Inversion Control Arithmetic

2009 ◽  
Author(s):  
Guang-lin He ◽  
Guo-hui Wu ◽  
Li Lao ◽  
Jing Chen
Keyword(s):  
Neural Network ◽  
Network Dynamic ◽  
Dynamic Inversion ◽  
Neural Network Dynamic ◽  
Control Arithmetic

scholarly journals Adaptive Neural Network Dynamic Inversion with Prescribed Performance for Aircraft Flight Control

2013 ◽  
Vol 2013 ◽  
pp. 1-12 ◽  
Author(s):  
Wendong Gai ◽  
Honglun Wang ◽  
Jing Zhang ◽  
Yuxia Li
Keyword(s):  
Neural Network ◽  
Flight Control ◽  
Network Dynamic ◽  
Dynamic Inversion ◽  
Adaptive Neural Network ◽  
Prescribed Performance ◽  
Aircraft Flight ◽  
Attitude Angle ◽  
Neural Network Dynamic ◽  
Aircraft Flight Control

An adaptive neural network dynamic inversion with prescribed performance method is proposed for aircraft flight control. The aircraft nonlinear attitude angle model is analyzed. And we propose a new attitude angle controller design method based on prescribed performance which describes the convergence rate and overshoot of the tracking error. Then the model error is compensated by the adaptive neural network. Subsequently, the system stability is analyzed in detail. Finally, the proposed method is applied to the aircraft attitude tracking control system. The nonlinear simulation demonstrates that this method can guarantee the stability and tracking performance in the transient and steady behavior.

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

2018 ◽  
Vol 38 (3) ◽  
pp. 268-278
Author(s):  
Maolong Lv ◽  
Xiuxia Sun ◽  
G. Z. Xu ◽  
Z. T. Wang
Keyword(s):  
Neural Network ◽  
Control Method ◽  
Nonlinear Modeling ◽  
Dynamic Surface Control ◽  
Continuous Functions ◽  
Network Dynamic ◽  
Dynamic Surface ◽  
The Neural Network ◽  
Neural Network Dynamic ◽  
Robust Adaptive

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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