Share:


New heuristic algorithms for rolling air frame aerodynamic parameters estimation

    Ayham Mohamad Affiliation
    ; Jalal Karimi   Affiliation
    ; Alireza Naderi Affiliation

Abstract

In this research, based on heuristic optimization algorithms, three new strategies are developed for Aerodynamic Parameters Estimation (APE) of one pair ON-OFF actuator rolling airframe. In the 1st method namely EAM-PSO the aerodynamic parameters are directly estimated. While, the next two algorithms called EBM-PSO and SEBM-PSO are two-step strategies. In the 1st step the aerodynamic forces and moments are estimated, then after passing through a designed smoothing filter, in the 2nd step aerodynamic parameters are estimated. In EBM-PSO all the aerodynamic parameters are estimated at once by solving one optimization problem. In SEBM-PSO the APE is converted to solve four separate optimization problems. A modified particle swarm optimization algorithm is developed and used in estimation process. The performance of proposed algorithms is compared with that of state of the art algorithm EKF. The simulation results show that SEBM-PSO and EBM-PSO are better than EAM-PSO in term of accuracy and run time.

Keyword : aerodynamic parameter estimation, estimation after modelling, estimation before modelling, particle swarm optimization, smoothing filter

How to Cite
[1]
Mohamad, A., Karimi, J. and Naderi, A. 2020. New heuristic algorithms for rolling air frame aerodynamic parameters estimation. Aviation. 24, 1 (Apr. 2020), 20-32. DOI:https://doi.org/10.3846/aviation.2020.12092.
Published in Issue
Apr 16, 2020
Abstract Views
619
PDF Downloads
167
Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

References

Aksu, A. (2013). Aerodynamic parameter estimation of a missile (Master Thesis). Middle East Technical University, Turkey. https://doi.org/10.2514/6.2014-2557

Bayoğlu, T., & Nalci, M. (2016, 13–17 June). Aerodynamic parameter estimation of a supersonic air to air missile with rapid speed. AIAA Atmospheric Flight Mechanics Conference. Washington, D.C. https://doi.org/10.2514/6.2016-3856

Bian, Q., Zhao, K., Wang, X., & Xie, R. (2016). System identification method for small unmanned helicopter based on IPSO. Journal of Bionic Engineering, 13(2016), 504–514. https://doi.org/10.1016/S1672-6529(16)60323-2

Chowdhary, G., & Jategaonkar, R. (2009). Aerodynamic parameter estimation from flight data applying EKF & UKF. Aerospace Science and Technology, 14(2), 106–117. https://doi.org/10.1016/j.ast.2009.10.003

Engelbrecht, A. (2005). Fundamentals of computational swarm intelligence. John Willey & Sons, Ltd.

Guan, J., Yi, W., Chang, S., & Li, X. (2016). Aerodynamic parameter estimation of a symmetric projectile using adaptive chaotic mutation PSO. Mathematical Problems in Engineering, Article ID 5910928. https://doi.org/10.1155/2016/5910928

Hanafy, Th., Al-Harthi, M., & Merabtine, N. (2014). Modeling and identification of spacecraft systems using ANFIS. IOSR Journal of Engineering (IOSRJEN), 04(05), 47–61. https://doi.org/10.9790/3021-04544759

Hatamleh, Kh., Al-Shabi, M., Al-Ghasem, A., & Asad, A. (2015). UAV parameter estimation using artificial neural networks and iterative bi-section shooting. Applied Soft Computing, 36, 457–467. https://doi.org/10.1016/j.asoc.2015.06.031

Kamali, C., Jain, Sh., Saraf, A., & Goyal, A. (2016). Calibration of static pressure sensors using EKF at high AOA and transonic Mach number. IEEE Indian Control Conference (ICC). India. https://doi.org/10.1109/INDIANCC.2016.7441170

Karimi, J., Pourtakdoust, S., & Nobahari, H. (2011). Trim and manoeuvrability analysis of a UAV using a new constrained PSO approach. JAST, Iranian Aerospace Society, 8(1), 45–56.

Kennedy, J., & Eberhart, R. (1995). Particle swarm optimization. IEEE International Conference on Neural Networks, 4 (Nov/Dec.), 1942–1948. Perth, WA, Australia. https://doi.org/10.1109/ICNN.1995.488968

Klein, V., & Morelli, E. (2006). Aircraft system identification theory and practice. American Institute of Aeronautics and Astronautics, Inc., Reston, Virginia, USA. https://doi.org/10.2514/4.861505

Kumari, N., Sunita, & Smita. (2013). Comparison of ANNs, fuzzy logic and neuro-fuzzy integrated approach. IJCSMC, 2(6), 216–224.

Larsson, R. (2013). System identification of flight mechanical characteristics (PhD Thesis). Department of Electrical Engineering Linköping University, Sweden.

Mohammadi, A., & Massoumnia, M. A. (2000, 14–17 August). Missile aerodynamic identification using mixed EKF and EBM. 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. AIAA SciTech Forum. San Diego, California. https://doi.org/10.2514/6.2019-0433

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. Advances in Optical and Mechanical Technologies for Telescopes and Instrumentation, 9912, 99126W.

Nobahari, H., & Mohammad Karimi, H. (2011). Multiple-input describing function technique applied to design a single channel ON-OFF controller for a spinning flight vehicle. Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering, 226. https://doi.org/10.1177/0954410011414521

Nobahari, H., & Sharifi, A. (2014). Continuous ant colony filter applied to online estimation and compensation of ground effect in automatic landing of quadrotor. Engineering Applications of Artificial Intelligence, 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.

Sone, Ch., & Yadav, O. (2015). A survey on intelligent techniques for character identification. International Journal of Engineering and Innovative Technology (IJEIT), 1(5).

Tan, D., & Chen, Zh. (2012). On a general formula of fourth order Runge-Kutta method. Journal of Mathematical Science & Mathematics Education, 7(2), 1–10.

Tieying, J., Jie, L., & Kewei, H. (2015). Longitudinal parameter identification of a small UAV based on modified PSO. Chinese Journal of Aeronautics, 28(3), 865–873. https://doi.org/10.1016/j.cja.2015.04.005

Valasek, J., & Chen, W. (2003). Observer KF identification for online system identification of aircraft. Journal of Guidance, Control, and Dynamics, 26(2), 347–353. https://doi.org/10.2514/2.5052

Vitale, A. (2013). Multi-step estimation approach for aerospace vehicle system identification from flight data (PhD Thesis). Federico II University, Napoli, Italy.

Wang, Y., Xu, J., Ge, Sh., & Lu, Ch. (2013). Review of aircraft aerodynamic force parameters identification based on the intelligent algorithm. International Workshop on Cloud Computing and Information Security (CCIS). China.