scholarly journals Development of a Nonlinear Reconfigurable F-16 Model and Flight Control Systems Using Multilayer Adaptive Neural Networks

2015 ◽  
Vol 48 (9) ◽  
pp. 138-143 ◽  
Author(s):  
David Torres Ocaña ◽  
Hyo-Sang Shin ◽  
Antonios Tsourdos
Keyword(s):  
Neural Networks ◽  
Control Systems ◽  
Flight Control ◽  
Adaptive Neural Networks

Research of Nonlinear Adaptive Flight Control Using Inversible System of Neural Networks

2014 ◽  
Vol 490-491 ◽  
pp. 960-963
Author(s):  
Shao Song Wan ◽  
Jian Cao ◽  
Cen Rui Ma ◽  
Cong Yan
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Stability Analysis ◽  
Flight Control ◽  
Feedback Linearization ◽  
Adaptive Neural Networks ◽  
Adaptive Flight Control ◽  
The Status ◽  
Training Structure

This paper discusses training structure and procedure about inversible system of neural network. Feedback linearization and adaptive neural networks provide a powerful controller architecture. Finally, this paper surveys the status of nonlinear, and adaptive flight control, and summarizes the research being conducted in this area. A description of the controller architecture and associated stability analysis is given.

scholarly journals Design of Satellite Attitude Control Systems using Adaptive Neural Networks

2020 ◽  
Vol 12 (3) ◽  
pp. 173-182
Author(s):  
M. RAJA ◽  
Kartikay SINGH ◽  
Aishwerya SINGH ◽  
Ayush GUPTA
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Adaptive Control ◽  
Control Systems ◽  
Attitude Control ◽  
Research Work ◽  
Poor Performance ◽  
Error Feedback ◽  
Pid Tuning ◽  
Adaptive Neural Networks

This paper investigates the performance of adaptive neural networks through simulations for satellite systems involving three-axis attitude control algorithms. PID tuning is the method employed traditionally. An optimally tuned, to minimizes the deviation from set point. It also responds quickly to the disturbances with some minimal overshoot. However, the disadvantage of poor performance has been observed in these controllers when manual tuning is used which in itself a monotonous process is. The PID controller using Ziegler-Nichols has more transient responses of satellite such as Overshoot, Settling time, and Steady state errors. For overcome this technique, the proposed analysis implemented an Adaptive Neural Network with PID tuning. The paper aims to combine two feedback methods by using neural networks. These methods are feed- forward and error feedback adaptive control. The research work is expected to reveal the inside working of these neural network controllers for state and error feedback input states. An error driven adaptive control systems is produced, when the neural networks acquire the knowledge of slopes and gains regarding the error feedback, while, with state feedback the system will keep trying to approximate a stable approach in order to stabilize the attitude of the satellite.

Analysis of Adaptive Neural Networks for Helicopter Flight Control

1997 ◽  
Vol 20 (5) ◽  
pp. 972-979 ◽  
Author(s):  
Jesse Leitner ◽  
Anthony Calise ◽  
J. V. R. Prasad
Keyword(s):  
Neural Networks ◽  
Flight Control ◽  
Helicopter Flight ◽  
Adaptive Neural Networks
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