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A neural network approach to the calibration of a flush air data system

Published online by Cambridge University Press:  04 July 2016

W. J. Crowther  and
P. J. Lamont
W. J. Crowther
Affiliation:
Manchester School of Engineering, University of Manchester, UK
P. J. Lamont
Affiliation:
Manchester School of Engineering, University of Manchester, UK
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Abstract

A flush air data system uses surface pressure measurements to obtain speed and aerodynamic orientation of a flight vehicle. This paper investigates the use of a neural network to calibrate the air data system on a low-observable aircraft forebody with 22 pressure tappings. Wind tunnel data were obtained for between 0 and 25° angle of attack, 0 to 10° sideslip for speeds of 32, 37 and 45ms-1. Experimental data were used to train multilayer perceptron neural networks. A calibration accuracy of 0·322ms-1, 0·811° and 0·552° for speed, alpha and beta respectively was achieved just using the first five tappings on the nose cone. Increasing the number of tappings used as inputs to the neural network reduces the calibration error. A neural network with 22 inputs gives a best accuracy of 0·095ms-1, 0·15° and 0·085° for speed, α and β respectively. Trained networks show poor robustness to single sensor failures. Robustness is improved by preprocessing input data with an autoassociative network or by introducing sensor redundancy.

Type
Research Article
Information
The Aeronautical Journal , Volume 105 , Issue 1044 , February 2001 , pp. 85 - 95
Copyright
Copyright © Royal Aeronautical Society 2001 

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