Neural Network Based Adaptive Flight Control of UAVs

2021 ◽  
Author(s):  
Mackenzie T. Matthews ◽  
Sun Yi
Keyword(s):  
Neural Network ◽  
Flight Control ◽  
Adaptive Flight Control

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.

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

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

scholarly journals Rule Insertion Technique for Dynamic Cell Structure Neural Network

2020 ◽  
Vol 7 ◽  
Keyword(s):  
Neural Network ◽  
Flight Control ◽  
Expert Knowledge ◽  
Cell Structure ◽  
Training Data ◽  
Self Organizing Map ◽  
Data Set ◽  
Adaptive Flight Control ◽  
Dynamic Cell ◽  
Iris Data

This paper discusses the idea of capturing an expert’s knowledge in the form of human understandable rules and then inserting these rules into a dynamic cell structure (DCS) neural network. The DCS is a form of self-organizing map that can be used for many purposes, including classification and prediction. This particular neural network is considered to be a topology preserving network that starts with no pre-structure, but assumes a structure once trained. The DCS has been used in mission and safety-critical applications, including adaptive flight control and health-monitoring in aerial vehicles. The approach is to insert expert knowledge into the DCS before training. Rules are translated into a pre-structure and then training data are presented. This idea has been demonstrated using the well-known Iris data set and it has been shown that inserting the pre-structure results in better accuracy with the same training.

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