Adaptive Flight Control for Unmanned Aircraft Using a Stable Neural Network Observer

2010 ◽  
Author(s):  
Thomas Krüger ◽  
Michael Mössner ◽  
Joachim Axmann ◽  
Peter Vörsmann ◽  
Andreas Kuhn
Keyword(s):  
Neural Network ◽  
Flight Control ◽  
Unmanned Aircraft ◽  
Adaptive Flight Control ◽  
Neural Network Observer

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 Designing a computerized information processing system to build a movement trajectory of an unmanned aircraft

2021 ◽  
Vol 1 (9 (109)) ◽  
pp. 33-42
Author(s):  
Volodymyr Kvasnikov ◽  
Dmytro Ornatskyi ◽  
Maryna Graf ◽  
Oleksii Shelukha
Keyword(s):  
Neural Network ◽  
Flight Control ◽  
High Efficiency ◽  
Processing System ◽  
Unmanned Aircraft ◽  
Movement Trajectory ◽  
Frame Size ◽  
Neural Network Learning ◽  
Network Learning ◽  
The Neural Network

This paper addresses the issue of developing a computerized system for processing information in the construction of the trajectory of an unmanned aircraft (UAC), a remotely-piloted aviation system (RPAS), or another robotic system. Resolving this task involves the neural network learning algorithms based on the mathematical model of movement. The construction of such a trajectory between two specified destinations has been considered that provides for the possibility of bypassing static and dynamic obstacles. The specified trajectory is divided into several smaller parts. The possibility of restructuring when changing the position of obstacles in space has been considered. A UAC flight control algorithm has been developed, which implies training a neural network for bypassing obstacles of different sizes. To predict the development of the situation when an object moves between two specified points in space, it is proposed to use the Q-Learning algorithm. It has been shown that the smallest number of steps required for moving along a specified trajectory is 18, the largest is 273 steps. In case of distortion during data transmission, the training of the neural network makes it possible to reduce the possibility of collision with obstacles by improving the accuracy and speed of information transfer between the on-board computer and operator. A system of the video support to moving objects was modeled; dependence charts of the normalized frame size at different parameter values were built. Using the charts makes it possible to determine the function of the maneuver intensity. Existing neural network learning methods such as CNN and LSTM were compared. It has been proven that the success rate reaches 74 % when using CNN only, while it amounts to 92 % at the hybrid application of CNN+LSTM. The simulation results have demonstrated the high efficiency of the developed algorithm

Adaptive Flight-Control Design Using Neural-Network-Aided Optimal Nonlinear Dynamic Inversion

2014 ◽  
Vol 11 (11) ◽  
pp. 785-806 ◽  
Author(s):  
Geethalakshmi S. Lakshmikanth ◽  
Radhakant Padhi ◽  
John M. Watkins ◽  
James E. Steck
Keyword(s):  
Neural Network ◽  
Flight Control ◽  
Nonlinear Dynamic ◽  
Control Design ◽  
Dynamic Inversion ◽  
Adaptive Flight Control ◽  
Nonlinear Dynamic Inversion
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