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Estimation of lateral-directional parameters using neural networks based modified delta method

Published online by Cambridge University Press:  03 February 2016

S. Singh  and
A. K. Ghosh
S. Singh
Affiliation:
Indian Institute of Technology, Kanpur, India
A. K. Ghosh
Affiliation:
Indian Institute of Technology, Kanpur, India
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Abstract

The aim of the study described herein was to develop and verify an efficient neural network based method for extracting aircraft stability and control derivatives from real flight data using feed-forward neural networks. The proposed method (Modified Delta method) draws its inspiration from feed forward neural network based the Delta method for estimating stability and control derivatives. The neural network is trained using differential variation of aircraft motion/control variables and coefficients as the network inputs and outputs respectively. For the purpose of parameter estimation, the trained neural network is presented with a suitably modified input file and the corresponding predicted output file of aerodynamic coefficients is obtained. An appropriate interpretation and manipulation of such input-output files yields the estimates of the parameter. The method is validated first on the simulated flight data using various combinations and types of real-flight control inputs and then on real flight data. A new technique is also proposed for validating the estimated parameters using feed-forward neural networks.

Type
Research Article
Information
The Aeronautical Journal , Volume 111 , Issue 1124 , October 2007 , pp. 659 - 667
Copyright
Copyright © Royal Aeronautical Society 2007 

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