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Lateral directional aircraft aerodynamic parameter estimation using adaptive stochastic nonlinear filter

Published online by Cambridge University Press:  18 November 2021

M. Mohamed Open the ORCID record for M. Mohamed [Opens in a new window]  and
N. Joy
M. Mohamed*
Affiliation:
Flight Mechanics and Control Division CSIR- National Aerospace LaboratoriesBangaloreIndia
N. Joy
Affiliation:
Department of Electrical Engineering College of Engineering Trivandrum ThiruvananthapuramIndia
*
majeed_md_123@rediffmail.com
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Abstract

This paper aims to accurately estimate the lateral directional aerodynamic parameters in real time irrespective of the variations in the process and measurement covariance matrices. The proposed algorithm for parameter estimation is based on the integration of adaptive techniques into a stochastic nonlinear filter. The proposed adaptive estimation algorithm is applied to flight test data, and the lateral directional derivatives are estimated in real time. The estimates are compared with those obtained from the Filter Error Method (FEM), an offline parameter estimation method accounting for process noise. The estimation results are observed to be very comparable, and the supremacy of the adaptive filter is illustrated by varying the covariance matrices of both process and measurement noises. The parameters estimated by the adaptive filter are found to converge to their actual values, whereas the estimates of the regular filter are observed to diverge from the actual values when changing the noise covariance matrices. The proposed adaptive algorithm can estimate the lateral directional aerodynamic derivatives more accurately without prior knowledge of either process or measurement noise covariance matrices. Hence, it is of great value in online implementations.

Keywords

Aircraft parameter estimationSystem identificationAircraft stability and controlNonlinear stochastic filtering
Type
Research Article
Information
The Aeronautical Journal , Volume 125 , Issue 1294 , December 2021 , pp. 2217 - 2228
Copyright
© The Author(s), 2021. Published by Cambridge University Press on behalf of Royal Aeronautical Society

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