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Nonlinear parameter estimation of airship using modular neural network

Published online by Cambridge University Press:  29 October 2019

S. Agrawal Open the ORCID record for S. Agrawal [Opens in a new window] ,
D. Gobiha  and
N.K. Sinha Open the ORCID record for N.K. Sinha [Opens in a new window]
S. Agrawal*
Affiliation:
subhamagrwl246@gmail.com Flight Dynamics and Control Lab Department of Aerospace EngineeringIndian Institute of Technology Madras Chennai Indianandan@ae.iitm.ac.in
D. Gobiha
Affiliation:
gobiha@yahoo.com Flight Dynamics and Control Lab Department of Aerospace EngineeringIndian Institute of Technology Madras Chennai Indianandan@ae.iitm.ac.in
N.K. Sinha
Affiliation:
nandan@ae.iitm.ac.in Flight Dynamics and Control Lab Department of Aerospace EngineeringIndian Institute of Technology Madras Chennai Indianandan@ae.iitm.ac.in
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Abstract

The prime focus of this work is to estimate stability and control derivatives of an airship in a completely nonlinear environment. A complete six degrees of freedom airship model has its aerodynamic model as nonlinear functions of angle of attack. Estimating the parameters of aerodynamic model in a nonlinear environment is challenging as it demands an exhaustive dataset that could cover the entire regime of operation of airship. In this work, data generation is achieved by simulating the mathematical model of airship for different trim conditions obtained from continuation analysis. The mathematical model is simulated using predicted parameter values obtained using DATCOM methodology. A modular neural network is then trained using back-propagation and Adam optimisation algorithm for each of the aerodynamic coefficients separately. The estimated nonlinear airship parameters are found to be consistent with the DATCOM parameter values which were used for open-loop simulation. This validates the proposed methodology and could be extended to estimate airship parameters from real flight data.

Keywords

Parameter estimationNeural networkAirship
Type
Research Article
Information
The Aeronautical Journal , Volume 124 , Issue 1273 , March 2020 , pp. 409 - 428
Copyright
© Royal Aeronautical Society 2019 

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