Recurrent Neural Network for Estimation of Aerodynamic Parameters

In this paper, Recurrent Neural Network (RNN) is proposed for the estimation of aerodynamic derivatives of an aircraft. The simulated flight data for short period mode of research aircraft, Hansa is used to train the RNN. The trained RNN is then used to predict the output for a given input file. The...

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Published in:International Conference on Signal Processing and Integrated Networks (Online) pp. 150 - 155
Main Authors: Mahajan, Hardik K., Kaur, Jaspreet, Singh, S., Banerjee, S.
Format: Conference Proceeding
Language:English
Published: IEEE 26.08.2021
Subjects:
Aerodynamics
Backpropagation
Delta Method
Estimation
Feed Forward Back Propagation Neural Network
Force
Parameter Estimation
Recurrent Neural Network
Recurrent neural networks
Signal processing
Signal processing algorithms
ISSN:2688-769X
Online Access:Get full text
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Abstract In this paper, Recurrent Neural Network (RNN) is proposed for the estimation of aerodynamic derivatives of an aircraft. The simulated flight data for short period mode of research aircraft, Hansa is used to train the RNN. The trained RNN is then used to predict the output for a given input file. The predicted data is then used for the estimation of aerodynamic derivatives using the Delta Method. The results obtained using RNN are compared to the results obtained using Feed Forward Back propagation algorithm (FFBP). It is found that the derivatives obtained using RNN are very close to true values of derivatives with lesser standard deviation as compared to derivatives obtained using FFBP algorithm. The RNN is also validated by adding the various percentages of noises in the simulated flight data. The results increase level of confidence and suggest that the RNN can be used advantageously to estimate aerodynamic derivatives of an aircraft from real flight data.
AbstractList In this paper, Recurrent Neural Network (RNN) is proposed for the estimation of aerodynamic derivatives of an aircraft. The simulated flight data for short period mode of research aircraft, Hansa is used to train the RNN. The trained RNN is then used to predict the output for a given input file. The predicted data is then used for the estimation of aerodynamic derivatives using the Delta Method. The results obtained using RNN are compared to the results obtained using Feed Forward Back propagation algorithm (FFBP). It is found that the derivatives obtained using RNN are very close to true values of derivatives with lesser standard deviation as compared to derivatives obtained using FFBP algorithm. The RNN is also validated by adding the various percentages of noises in the simulated flight data. The results increase level of confidence and suggest that the RNN can be used advantageously to estimate aerodynamic derivatives of an aircraft from real flight data.
Author Kaur, Jaspreet
Singh, S.
Mahajan, Hardik K.
Banerjee, S.
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  surname: Banerjee
  fullname: Banerjee, S.
  email: sbanerjee4@amity.edu
  organization: Amity University,Amity Institute of Aerospace Engineering,Uttar Pradesh,INDIA
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Snippet In this paper, Recurrent Neural Network (RNN) is proposed for the estimation of aerodynamic derivatives of an aircraft. The simulated flight data for short...
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StartPage 150
SubjectTerms Aerodynamics
Backpropagation
Delta Method
Estimation
Feed Forward Back Propagation Neural Network
Force
Parameter Estimation
Recurrent Neural Network
Recurrent neural networks
Signal processing
Signal processing algorithms
Title Recurrent Neural Network for Estimation of Aerodynamic Parameters
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