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2021 | OriginalPaper | Chapter

4. Identification of Aircraft Lateral-Directional Derivatives

Authors : Majeed Mohamed, Vikalp Dongare

Published in: Aircraft Aerodynamic Parameter Estimation from Flight Data Using Neural Partial Differentiation

Publisher: Springer Singapore

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Abstract

The application of neural networks combined with partial differentiation of the neural outputs is discussed in this chapter to estimate lateral-directional flight stability and control derivatives from flight data. Primary investigation is carried out with simulated data and results are found to be encouraging to apply with flight data.

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previous chapter Identification of Aircraft Longitudinal Derivatives

next chapter Identification of a Flexible Aircraft Derivatives

V. Dongare, M. Mohamed, Lateral-directional aerodynamics parameter estimation using neural partial differentiation, in 2015 International Conference on Cognitive Computing and Information Processing (ccip) (2015), pp. 1–6. https://doi.org/10.1109/CCIP.2015.7100730

A.K. Ghosh, S.C. Raisinghani, S. Khubchandani, Estimation of aircraft lateral-directional parameters using neural networks. J. Aircr. 35876–881 (1998)

R. Jategaonkar, Flight vehicle system identification: a time domain methodology. American Institute of Aeronautics and (2006). Astronautics

V. Klein, E.A. Moreli, Aircraft System Identification Theory and Practice, Education series (Reston, VAAIAA, 2006)

M. Mohamed, V. Dongare, Aircraft neural modeling and parameter estimation using neural partial differentiation. Aircr. Eng. Aerosp. Technol. 90(5), 764–778 (2018). https://doi.org/10.1108/AEAT-02-2016-0021

S. Singh, A. Ghosh, Estimation of lateral-directional parameters using neural networks based modified delta method. Aeronat. J. 111(3150), 659–667 (2007)

Title: Identification of Aircraft Lateral-Directional Derivatives
Authors: Majeed Mohamed
Vikalp Dongare
Publisher: Springer Singapore
Book: Aircraft Aerodynamic Parameter Estimation from Flight Data Using Neural Partial Differentiation
Print ISBN: 978-981-16-0103-3

Electronic ISBN: 978-981-16-0104-0

Copyright Year: 2021
DOI: https://doi.org/10.1007/978-981-16-0104-0_4

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