Estimation of Longitudinal Aerodynamic Derivatives Using Genetic Algorithm Optimized Method

Ambuj Srivastava; Ajit Kumar; Ajoy Kanti Ghosh

doi:doi:10.11648/j.ajetm.20190402.11

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Estimation of Longitudinal Aerodynamic Derivatives Using Genetic Algorithm Optimized Method

Ambuj Srivastava, Ajit Kumar, Ajoy Kanti Ghosh

Published in American Journal of Engineering and Technology Management (Volume 4, Issue 2)

Received: 10 April 2019 Accepted: 21 May 2019 Published: 10 June 2019

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Abstract

This paper presents the estimation of longitudinal aerodynamic parameters by using Genetic Algorithm (GA) optimized method from simulated and real flight data of ATTAS aircraft. The simulated flight data is deliberately contaminated with 5%, 10%, and 15% of random noise for creating flight data, which bears similarity to real flight data. The proposed methodology utilizes the general notion of output error method, i.e., minimizing the response error between the measured response and estimated response, and the genetic algorithm as the optimization technique for an iterative update of the parameter vector. The longitudinal parameters are estimated by using the proposed method from both simulated data (without and with random noise) and real flight data. The parameter estimates obtained by using the proposed method is compared with the estimates from the Maximum-Likelihood method and data-driven methods viz. Delta method and GPR –Delta method for assessing the efficacy of the methodology. The statistical analysis of the parameter estimates has further cemented the confidence in the estimates obtained by using the proposed method.

Published in	American Journal of Engineering and Technology Management (Volume 4, Issue 2)
DOI	10.11648/j.ajetm.20190402.11
Page(s)	34-46
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2019. Published by Science Publishing Group

Keywords

Genetic Algorithm, Parameter Estimation, Flight Dynamics, Aerodynamic Derivatives, Maximum Likelihood, Data-Driven Method

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APA Style

Ambuj Srivastava, Ajit Kumar, Ajoy Kanti Ghosh. (2019). Estimation of Longitudinal Aerodynamic Derivatives Using Genetic Algorithm Optimized Method. American Journal of Engineering and Technology Management, 4(2), 34-46. https://doi.org/10.11648/j.ajetm.20190402.11

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ACS Style

Ambuj Srivastava; Ajit Kumar; Ajoy Kanti Ghosh. Estimation of Longitudinal Aerodynamic Derivatives Using Genetic Algorithm Optimized Method. Am. J. Eng. Technol. Manag. 2019, 4(2), 34-46. doi: 10.11648/j.ajetm.20190402.11

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AMA Style

Ambuj Srivastava, Ajit Kumar, Ajoy Kanti Ghosh. Estimation of Longitudinal Aerodynamic Derivatives Using Genetic Algorithm Optimized Method. Am J Eng Technol Manag. 2019;4(2):34-46. doi: 10.11648/j.ajetm.20190402.11

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@article{10.11648/j.ajetm.20190402.11,
  author = {Ambuj Srivastava and Ajit Kumar and Ajoy Kanti Ghosh},
  title = {Estimation of Longitudinal Aerodynamic Derivatives Using Genetic Algorithm Optimized Method},
  journal = {American Journal of Engineering and Technology Management},
  volume = {4},
  number = {2},
  pages = {34-46},
  doi = {10.11648/j.ajetm.20190402.11},
  url = {https://doi.org/10.11648/j.ajetm.20190402.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajetm.20190402.11},
  abstract = {This paper presents the estimation of longitudinal aerodynamic parameters by using Genetic Algorithm (GA) optimized method from simulated and real flight data of ATTAS aircraft. The simulated flight data is deliberately contaminated with 5%, 10%, and 15% of random noise for creating flight data, which bears similarity to real flight data. The proposed methodology utilizes the general notion of output error method, i.e., minimizing the response error between the measured response and estimated response, and the genetic algorithm as the optimization technique for an iterative update of the parameter vector. The longitudinal parameters are estimated by using the proposed method from both simulated data (without and with random noise) and real flight data. The parameter estimates obtained by using the proposed method is compared with the estimates from the Maximum-Likelihood method and data-driven methods viz. Delta method and GPR –Delta method for assessing the efficacy of the methodology. The statistical analysis of the parameter estimates has further cemented the confidence in the estimates obtained by using the proposed method.},
 year = {2019}
}

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TY  - JOUR
T1  - Estimation of Longitudinal Aerodynamic Derivatives Using Genetic Algorithm Optimized Method
AU  - Ambuj Srivastava
AU  - Ajit Kumar
AU  - Ajoy Kanti Ghosh
Y1  - 2019/06/10
PY  - 2019
N1  - https://doi.org/10.11648/j.ajetm.20190402.11
DO  - 10.11648/j.ajetm.20190402.11
T2  - American Journal of Engineering and Technology Management
JF  - American Journal of Engineering and Technology Management
JO  - American Journal of Engineering and Technology Management
SP  - 34
EP  - 46
PB  - Science Publishing Group
SN  - 2575-1441
UR  - https://doi.org/10.11648/j.ajetm.20190402.11
AB  - This paper presents the estimation of longitudinal aerodynamic parameters by using Genetic Algorithm (GA) optimized method from simulated and real flight data of ATTAS aircraft. The simulated flight data is deliberately contaminated with 5%, 10%, and 15% of random noise for creating flight data, which bears similarity to real flight data. The proposed methodology utilizes the general notion of output error method, i.e., minimizing the response error between the measured response and estimated response, and the genetic algorithm as the optimization technique for an iterative update of the parameter vector. The longitudinal parameters are estimated by using the proposed method from both simulated data (without and with random noise) and real flight data. The parameter estimates obtained by using the proposed method is compared with the estimates from the Maximum-Likelihood method and data-driven methods viz. Delta method and GPR –Delta method for assessing the efficacy of the methodology. The statistical analysis of the parameter estimates has further cemented the confidence in the estimates obtained by using the proposed method.
VL  - 4
IS  - 2
ER  -

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Author Information

Ambuj Srivastava

Transport Aircraft Research and Design Centre, Kanpur, India
Ajit Kumar

Department of Aerospace Engineering, Indian Institute of Technology, Kanpur, India
Ajoy Kanti Ghosh

Department of Aerospace Engineering, Indian Institute of Technology, Kanpur, India

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APA Style

Ambuj Srivastava, Ajit Kumar, Ajoy Kanti Ghosh. (2019). Estimation of Longitudinal Aerodynamic Derivatives Using Genetic Algorithm Optimized Method. American Journal of Engineering and Technology Management, 4(2), 34-46. https://doi.org/10.11648/j.ajetm.20190402.11

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ACS Style

Ambuj Srivastava; Ajit Kumar; Ajoy Kanti Ghosh. Estimation of Longitudinal Aerodynamic Derivatives Using Genetic Algorithm Optimized Method. Am. J. Eng. Technol. Manag. 2019, 4(2), 34-46. doi: 10.11648/j.ajetm.20190402.11

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AMA Style

Ambuj Srivastava, Ajit Kumar, Ajoy Kanti Ghosh. Estimation of Longitudinal Aerodynamic Derivatives Using Genetic Algorithm Optimized Method. Am J Eng Technol Manag. 2019;4(2):34-46. doi: 10.11648/j.ajetm.20190402.11

Copy | Download

@article{10.11648/j.ajetm.20190402.11,
  author = {Ambuj Srivastava and Ajit Kumar and Ajoy Kanti Ghosh},
  title = {Estimation of Longitudinal Aerodynamic Derivatives Using Genetic Algorithm Optimized Method},
  journal = {American Journal of Engineering and Technology Management},
  volume = {4},
  number = {2},
  pages = {34-46},
  doi = {10.11648/j.ajetm.20190402.11},
  url = {https://doi.org/10.11648/j.ajetm.20190402.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajetm.20190402.11},
  abstract = {This paper presents the estimation of longitudinal aerodynamic parameters by using Genetic Algorithm (GA) optimized method from simulated and real flight data of ATTAS aircraft. The simulated flight data is deliberately contaminated with 5%, 10%, and 15% of random noise for creating flight data, which bears similarity to real flight data. The proposed methodology utilizes the general notion of output error method, i.e., minimizing the response error between the measured response and estimated response, and the genetic algorithm as the optimization technique for an iterative update of the parameter vector. The longitudinal parameters are estimated by using the proposed method from both simulated data (without and with random noise) and real flight data. The parameter estimates obtained by using the proposed method is compared with the estimates from the Maximum-Likelihood method and data-driven methods viz. Delta method and GPR –Delta method for assessing the efficacy of the methodology. The statistical analysis of the parameter estimates has further cemented the confidence in the estimates obtained by using the proposed method.},
 year = {2019}
}

Copy | Download

TY  - JOUR
T1  - Estimation of Longitudinal Aerodynamic Derivatives Using Genetic Algorithm Optimized Method
AU  - Ambuj Srivastava
AU  - Ajit Kumar
AU  - Ajoy Kanti Ghosh
Y1  - 2019/06/10
PY  - 2019
N1  - https://doi.org/10.11648/j.ajetm.20190402.11
DO  - 10.11648/j.ajetm.20190402.11
T2  - American Journal of Engineering and Technology Management
JF  - American Journal of Engineering and Technology Management
JO  - American Journal of Engineering and Technology Management
SP  - 34
EP  - 46
PB  - Science Publishing Group
SN  - 2575-1441
UR  - https://doi.org/10.11648/j.ajetm.20190402.11
AB  - This paper presents the estimation of longitudinal aerodynamic parameters by using Genetic Algorithm (GA) optimized method from simulated and real flight data of ATTAS aircraft. The simulated flight data is deliberately contaminated with 5%, 10%, and 15% of random noise for creating flight data, which bears similarity to real flight data. The proposed methodology utilizes the general notion of output error method, i.e., minimizing the response error between the measured response and estimated response, and the genetic algorithm as the optimization technique for an iterative update of the parameter vector. The longitudinal parameters are estimated by using the proposed method from both simulated data (without and with random noise) and real flight data. The parameter estimates obtained by using the proposed method is compared with the estimates from the Maximum-Likelihood method and data-driven methods viz. Delta method and GPR –Delta method for assessing the efficacy of the methodology. The statistical analysis of the parameter estimates has further cemented the confidence in the estimates obtained by using the proposed method.
VL  - 4
IS  - 2
ER  -

Copy | Download