Abstract
The aerodynamic parameters of each flying vehicle dynamically change along its flight profile, because of aerodynamic parameter relationship with flight conditions, and several flight conditions take place during each flight profile. Therefore, in this research, the concept of dynamic aerodynamic parameter estimation (DAPE) is introduced. A two-step strategy is used: In the first step, the aerodynamic forces and moments are estimated; then, after passing through a designed smoothing filter, in the second step, the DAPE is converted to a dynamic optimization problem and solved by a heuristic optimization algorithm that hybridizes the features of particle swarm optimization in tracking dynamic changes with a new evolutionary procedure. Two new algorithms are developed: DAPE and SDAPE. In DAPE algorithm, all aerodynamic parameters are estimated at once by solving a single optimization problem. In SDAPE algorithm, four separate optimization problems are solved. A rolling airframe is the plant studied in this research. Simulation results indicate that SDAPE is better than DAPE in terms of accuracy. Comparing the performance of the newly proposed algorithms with that of three state-of-the-art static optimization algorithms and extended Kalman filter reveals their less run time and acceptable accuracy.
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References
Valasek J, Chen W (2003) Observer KF identification for online system identification of aircraft. J Guid Control Dyn 26(2):347–353. https://doi.org/10.2514/2.5052
Chowdhary G, Jategaonkar R (2009) Aerodynamic parameter estimation from flight data applying EKF & UKF. Aerosp Sci Technol 14:106–117. https://doi.org/10.1016/j.ast.2009.10.003
Bayoğlu T, Nalci M (2016, 13–17 June) Aerodynamic parameter estimation of a supersonic air to air missile with rapid speed. In: AIAA atmospheric flight mechanics conference. Washington, D.C. https://doi.org/10.2514/6.2016-3856
Hanafy Th, Al-Harthi M, Merabtine N (2014) Modeling and identification of spacecraft systems using ANFIS. IOSR J Eng (IOSRJEN) 04(05):47–61
Hatamleh KH, Al-Shabi M, Al-Ghasem A, Asad A (2015) UAV parameter estimation using artificial neural networks and iterative bi-section shooting. Appl Soft Comput 36:457–467. https://doi.org/10.1016/j.asoc.2015.06.031
Wang Y, Xu J, Ge Sh, Lu Ch (2013) Review of aircraft aerodynamic force parameters identification based on the intelligent algorithm. In: International workshop on cloud computing and information security (CCIS), China
Nobahari H, Sharifi A (2014) Continuous ant colony filter applied to online estimation and compensation of ground effect in automatic landing of quadrotor. Eng Appl Artif Intell 32:100–111. https://doi.org/10.1016/j.engappai.2014.03.004
Rezaei H (2015) Nonlinear system identification of an aerial vehicle by using heuristic algorithms (Master Thesis). MAUT, Iran
Tieying J, Jie L, Kewei H (2015) Longitudinal parameter identification of a small UAV based on modified PSO. Chin J Aeronaut 28(3):865–873
Guan J, Yi W, Chang S, Li X (2016) Aerodynamic parameter estimation of a symmetric projectile using adaptive chaotic mutation PSO. Math Probl Eng, Article ID 5910928. https://doi.org/10.1155/2016/5910928
Bian Q, Zhao K, Wang X, Xie R (2016) System identification method for small unmanned helicopter based on IPSO. J Bionic Eng 13(2016):504–514. https://doi.org/10.1016/S1672-6529(16)60323-2
Mohammadi A, Massoumnia M (2000, 14–17 August) A missile aerodynamic identification using mixed EKF and EBM. In: AIAA modeling and simulation technologies conference and exhibit. Denver, CO. https://doi.org/10.2514/6.2000-4288
Moszczynski G, Leung J, Grant P (2019, 7–11 January) Robust aerodynamic model identification, a new method for aircraft system identification in the presence of general dynamic model deficiencies. In: AIAA SciTech Forum. San Diego, California. https://doi.org/10.2514/6.2019-0433
Mohamad A, Karimi J, Naderi A (2020) New heuristic algorithms for rolling air frame aerodynamic parameters estimation. AVIATION 24(1):20–32. https://doi.org/10.3846/aviation.2020.12092
Karimi J, Nobahari H, Pourtakdoust SH (2012) A new hybrid approach for dynamic continuous optimization problems. Sharif University of Technology, Tehran
Aksu A (2013) Aerodynamic parameter estimation of a missile (Master Thesis). Middle East Technical University, Turkey. https://doi.org/10.2514/6.2014-2557
Klein V, Morelli E (2006) Aircraft system identification theory and practice. American Institute of Aeronautics and Astronautics, Inc, Reston. https://doi.org/10.2514/4.861505
Nirmal K, Sreejith A, Mathew J, Sarpotdar M, Suresh A (2016) Noise modeling and analysis of an IMU-based attitude sensor: improvement of performance by filtering and sensor fusion. Adv Opt Mech Technol Telesc Instrum 9912:99126W
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Mohamad, A., Karimi, J. & Naderi, A. Dynamic aerodynamic parameter estimation using a dynamic particle swarm optimization algorithm for rolling airframes. J Braz. Soc. Mech. Sci. Eng. 42, 579 (2020). https://doi.org/10.1007/s40430-020-02658-y
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DOI: https://doi.org/10.1007/s40430-020-02658-y