Recurrent Neural Network Adaptive Control of Wing-Rock Motion

2002 ◽  
Vol 25 (6) ◽  
pp. 1163-1165 ◽  
Author(s):  
Chih-Min Lin ◽  
Chun-Fei Hsu
Keyword(s):  
Neural Network ◽  
Adaptive Control ◽  
Recurrent Neural Network ◽  
Wing Rock ◽  
Wing Rock Motion

scholarly journals Neural network adaptive control of wing-rock motion of aircraft model mounted on three-degree-of-freedom dynamic rig in wind tunnel

2018 ◽  
Author(s):  
D. I. Ignatyev
Keyword(s):  
Neural Network ◽  
Adaptive Control ◽  
Wind Tunnel ◽  
Degree Of Freedom ◽  
Control Laws ◽  
Aircraft Model ◽  
Wing Rock ◽  
Wing Rock Motion ◽  
Three Degree Of Freedom ◽  
High Angles Of Attack

High-angles-of-attack dynamics of aircraft are complicated with dangerous phenomena such as wing rock, stall, and spin. Autonomous dynamically scaled aircraft model mounted in three-degree-of-freedom (3DoF) dynamic rig is proposed for studying aircraft dynamics and prototyping of control laws in wind tunnel. Dynamics of the scaled aircraft model in 3DoF manoeuvre rig in wind tunnel is considered. The model limit-cycle oscillations are obtained at high angles of attack. A neural network (NN) adaptive control suppressing wing rock motion is designed. The wing rock suppression with the proposed control law is validated using nonlinear time-domain simulations.

Diagonal recurrent neural network observer-based adaptive control for unknown nonlinear systems

2020 ◽  
Vol 42 (15) ◽  
pp. 2833-2856
Author(s):  
Ahmed Elkenawy ◽  
Ahmad M El-Nagar ◽  
Mohammad El-Bardini ◽  
Nabila M El-Rabaie
Keyword(s):  
Neural Network ◽  
Optimal Control ◽  
Adaptive Control ◽  
Nonlinear Systems ◽  
Recurrent Neural Network ◽  
Learning Algorithm ◽  
Lyapunov Stability Theory ◽  
Adaptive Dynamic Programming ◽  
External Disturbances ◽  
System States

This paper proposes an observer-based adaptive control for unknown nonlinear systems using an adaptive dynamic programming (ADP) algorithm. First, a diagonal recurrent neural network (DRNN) observer is proposed to estimate the unknown dynamics of the nonlinear system states. The proposed neural network offers a simpler structure with deeper memory and guarantees the faster convergence. Second, a neural controller is constructed via ADP method using the observed states to get the optimal control. The optimal control law is determined based on the new structure of the critic network, which is performed using the DRNN. The learning algorithm for the proposed DRNN observer-based adaptive control is developed based on the Lyapunov stability theory. Simulation results and hardware-in-the-loop results indicate the robustness of the proposed ADP to respond the system uncertainties and external disturbances compared with other existing schemes.

Variable structure adaptive control of wing-rock motion of slender delta wings

1997 ◽  
Author(s):  
Aldayr de Araujo ◽  
Sahjendra Singh ◽  
Aldayr de Araujo ◽  
Sahjendra Singh
Keyword(s):  
Adaptive Control ◽  
Variable Structure ◽  
Delta Wings ◽  
Wing Rock ◽  
Wing Rock Motion
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