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Parameter estimation of aircraft using extreme learning machine and Gauss-Newton algorithm

Published online by Cambridge University Press:  01 October 2019

H. O. Verma Open the ORCID record for H. O. Verma [Opens in a new window]  and
N. K. Peyada
H. O. Verma
Affiliation:
Department of Aerospace Engineering, Indian Institute of Technology Kharagpur, [email protected]; [email protected]; [email protected]
N. K. Peyada
Affiliation:
Department of Aerospace Engineering, Indian Institute of Technology Kharagpur, [email protected]; [email protected]; [email protected]
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Abstract

The research paper addresses the problem of estimating aerodynamic parameters using a Gauss-Newton-based optimisation method. The process of the optimisation method lies on the principle of minimising the residual error between the measured and simulated responses of the system. Usually, the simulated response is obtained by integrating the dynamic equations of the system, which is found to be susceptible to the initial values, and the integration method. With the advent of the feedforward neural network, the data-driven regression methods have been widely used for identification of the system. Among them, a variant of feedforward neural network, extreme learning machine, which has proven the performance in terms of computational cost, generalisation, and so forth, has been addressed to predict the responses in the present study. The real flight data of longitudinal and lateral-directional motion have been considered to estimate their respective aerodynamic parameters. Furthermore, the estimates have been validated with the values of the classical estimation methods, such as the equation-error and filter-error methods. The sample standard deviations of the estimates demonstrate the effectiveness of the proposed method. Lastly, the proof-of-match exercise has been conducted with the other set of flight data to validate the estimated parameters.

Keywords

Non-linear modellingExtreme Learning Machine NetworkGauss-Newton MethodParameter Estimation
Type
Research Article
Information
The Aeronautical Journal , Volume 124 , Issue 1272 , February 2020 , pp. 271 - 295
Copyright
© Royal Aeronautical Society 2019 

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