| Peer-Reviewed

Estimation of Longitudinal Aerodynamic Derivatives Using Genetic Algorithm Optimized Method

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

References
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Cite This Article
  • 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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    AU  - Ambuj Srivastava
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    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.
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Author Information
  • Transport Aircraft Research and Design Centre, Kanpur, India

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

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

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