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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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References

1. Cobleioh, B. and Del Frate, J. Water tunnel flow visualisation study of a4.4% scale X-31 forebody, Oct 1994, NASA TM-104276.SGoogle Scholar
2. Siemers, P., Wolf, H. and Henry, M. Shuttle entry air data system (SEADS) — Flight verification of an advanced air data system concept, May 1988, AIAA Paper 88-2104,.Google Scholar
3. Whitmore, S., Davis, R. and Fife, J. In-flight demonstration of a realtime flush air data sensing system, J Aircr, 1996, 33, (5), pp. 970-977.Google Scholar
4. Whitmore, S., Cobleioh, B. and Haering, E. Design and calibration of the X-33 flush air data sensing system, Jan 1998, NASA TM-1998-206540.Google Scholar
5. Wenger, C., Devenport, W. Seven-hole pressure probe calibration method utilizing look-up error tables, AIAA J, June 1999, 37, (6), pp675–679.Google Scholar
6. Rohloff, T. Development and Evaluation of Neural Network Flush Air Data Sensing Systems, May 1998, PhD Dissertation, Dept of Mechanical Engineering, Univ of California, Los Angeles, CA.Google Scholar
7. Rediniotis, O. and Vuayagopal, R. Miniature multihole pressure probes and their neural network based calibration, AIAA J, June 1999, 37, (6), pp666–674.Google Scholar
8. Rediniotis, O. and Chrysanthakopoulos, G. Application of neural networks and fuzzy logic to the calibration of the seven-hole probe, J Fluids Engineering — Transactions of the ASME, 1998, 120, (1), pp95–101.Google Scholar
9. Minsky, M. and Papert, S. Perceptrons-An Introduction to Computational Geometry, MIT Press, 1969, London, ISBN 0-262-630222.Google Scholar
10. Rumelhart, D., Hinton, G. and Williams, J. Learning internal representations by error propagation, In Parallel Distributed Processing, Explorations in the Microstructure of Cognition. (1) foundations, MIT Press, 1987, London, ISBN 0-262-18120-7.Google Scholar
11. Rumelhart, D.E., Widrow, B. and Lehr, M.A. The basic ideas in neural networks, Communications of the ACM, Mar 1994, 37, (3), pp86–92.Google Scholar
12. Gurney, K. An Introduction To Neural Networks, 1997, UCL Press, London. ISBN 1-85728-503-4.Google Scholar
13. Bishop, C. Neural Networks for Pattern Recognition, 1996, Oxford University Press, ISBN 0-19-853849-9.Google Scholar
14. Masters, T. Practical Neural Network Recipes in C++, 1993, Academic Press, ISBN 0-12-479040-2.Google Scholar
15. Blum, E. and Li, L. Approximation theory and feed-forward networks, Neural Networks, 4:4, pp511–515.Google Scholar
16. Whitmore, S. and Moes, , Failure detection and fault management techniques for a pneumatic high angle-of-attack flush air data system, Jan 1992, NASA TM-4335.Google Scholar