VPSA-Based Transfer Function Identification Of Single DOF Copter System

Year 2023, Volume: 04 Issue: 02, 87 - 97, 30.12.2023

Kübra Çiftçi , Muhammed Arif Şen , Hasan Hüseyin Bilgiç

Abstract

In this study, an experimental set of a Single-DOF Copter system is created and transfer functions that could model the dynamics of the physical system with high accuracy were investigated. In order to model the dynamics of the physical system with the highest accuracy, five different transfer functions have been proposed, in which the zero and pole values are determined by optimizing with the Vibrating Particle System Algorithm. ISE, ITAE, IAE, ITSE functions, which are widely used in the literature in determining transfer functions, are determined as objective functions. In order to verify the transfer functions, the responses of the transfer functions and the experimental system response are presented comparatively, and their suitability was evaluated. It has been observed that the proposed method is successful in defining the transfer function of the experimental system, and the compatibility of the obtained transfer functions with the system response is between 75.407% and 98.612% accuracy.

Keywords

VPSA, system identificaiton, transfer function, single-dof copter, optimisation

References

• Almufti, S., (2022). Vibrating Particles System Algorithm: Overview, Modifications and Applications. Icontech International Journal [online], 6 (3), 1–11. Available from: https://icontechjournal.com/index.php/iij/article/view/226.
• Altan, A. and Hacioglu, R., (2017). İnsansız Hava Aracı Üzerinde Bulunan 3 Eksenli Yalpa Sisteminin Dış Bozucu Altında Modellenmesi. In: 2017 25th Signal Processing and Communications Applications Conference, SIU 2017. Institute of Electrical and Electronics Engineers Inc.
• Bogdanski, K. and Best, M. C., (2017). A new structure for non-linear black-box system identification using the extended Kalman filter. Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering, 231 (14), 2005–2015.
• Doğa Okcu, I. and Leblebicioğlu, K., (2022). Kapalı Döngü Test Verisi Kullanarak Helikopter Dinamik Sistem Tanımlaması. Fırat Üniversitesi Uzay ve Savunma Dergisi, 1 (1), 422–429.
• Ebrahimi, B. and Barzamini, F., (2021). Aircraft System Identification Using Parametric Approaches and Intelligent Modeling. In: 2021 IEEE Aerospace Conference (50100). 1–12.
• Fahmi Pairan, M., Syafiq Shamsudin, S. and Fadhli Zulkafli, M., (2020). Neural Network-Based System Identification for Quadcopter Dynamic Modeling: A Review. Journal Of Advanced Mechanical Engineering Applications [online], 1 (2), 20–33. Available from: http://penerbit.uthm.edu.my/ojs/index.php/jamea.
• Fidan, Ş. and Erkan, E., (2023). Boost Konvertörün Black-Box Sistem Tanımlama Yöntemi ile Transfer Fonksiyonunun Elde Edilmesi ve Parçacık Sürü Algoritması Tabanlı PI Kontrolör Tasarımı [online]. Available from: http://as-proceeding.com/.
• Fidan, S., Sevim, D. and Erkan, E., (2022). System Identification and Control of High Voltage Boost Converter. In: IEEE Global Energy Conference, GEC 2022. Institute of Electrical and Electronics Engineers Inc., 25–31.
• Geluardi, S., Nieuwenhuizen, F. M., Venrooij, J., Pollini, L. and Bülthoff, H. H., (2018). Frequency Domain System Identification of a Robinson R44 in Hover. Journal of the American Helicopter Society [online], 63 (1), 1–18. Available from: https://search.ebscohost.com/login.aspx?direct=true&db=asn&AN=128077292&site=eds-live.
• Gyongyosi, L., (2020). Objective function estimation for solving optimization problems in gate-model quantum computers. Scientific Reports [online], 10 (1), 14220. Available from: https://doi.org/10.1038/s41598-020-71007-9.
• Hoffer, N. V., Coopmans, C., Jensen, A. M. and Chen, Y., (2014). A survey and categorization of small low-cost unmanned aerial vehicle system identification. Journal of Intelligent and Robotic Systems: Theory and Applications, 74 (1–2), 129–145.
• Ivler, C. M., Rowe, E. S., Martin, J., Lopez, M. J. S. and Tischler, M. B., (2021). System identification guidance for multirotor aircraft: Dynamic scaling and test techniques. Journal of the American Helicopter Society, 66 (2), 1–16.
• Kaveh, A. and Ilchi Ghazaan, M., (2017). Vibrating particles system algorithm for truss optimization with multiple natural frequency constraints. Acta Mechanica, 228 (1), 307–322.
• Kaveh, A., Kaveh, A. and Ilchi Ghazaan, M., (2017). A new meta-heuristic algorithm: vibrating particles system. Scientia Iranica [online], 24 (2), 551–566. Available from: https://scientiairanica.sharif.edu/article_2417.html.
• Nugroho, L. and Akmeliawati, R., (2018). Comparison of black-grey-white box approach in system identification of a flight vehicle. Journal of Physics: Conference Series [online], 1130 (1), 012024. Available from: https://dx.doi.org/10.1088/1742-6596/1130/1/012024.
• De Oliveira, E. C. L., de Araujo, J. P. L., da Silva Silveira, A., Silva, O. F., Vidal, J. F. and de França Silva, A., (2019). Quadrotor Black-Box System Identification Using Metaheuristics.
• Özcan, A. B. and Caferov, E., (2022). Frequency Domain Analysis of F-16 Aircraft in a Variety of Flight Conditions. Journal, 03 (01), 21–34.
• Pehlivan, K. and Akuner, C., (2020). Quadrotor Test Düzeneği Tasarımı ve Uygulaması. International Periodical of Recent Technologies in Applied Engineering, 2 (1), 15–24.
• Saengphet, W., Tantrairatn, S., Thumtae, C. and Srisertpol, J., (2017). Implementation of system identification and flight control system for UAV. In: 3rd International Conference on Control, Automation and Robotics (ICCAR). 678–683.
• Salameh, I. M., Ammar, E. M. and Tutunji, T. A., (2015). Identification of quadcopter hovering using experimental data. In: 2015 IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies (AEECT). 1–6.
• Sanatel, Ç., (2020). Uzun Kısa Süreli Bellek Tabanlı Sistem Tanıma ve Uyarlamalı Kontrol. İstanbul Teknik Üniversitesi, İstanbul .
• Sayll, A., Erden, F., Tüzün, A., Baykara, B. and Aydemir, M., (2023). A folding wing system for guided ammunitions: mechanism design, manufacturing and real-time results with LQR, LQI, SMC and SOSMC. Aeronautical Journal.
• Simmons, B. M., (2021). System Identification for eVTOL Aircraft Using Simulated Flight Data. In: AIAA SCITECH 2022 Forum [online]. American Institute of Aeronautics and Astronautics. Available from: https://doi.org/10.2514/6.2022-2409.
• Somov, Y., Somova, T. and Rodnishchev, N., (2021). Identification and Stochastic Optimizing the UAV Motion Control in Turbulent Atmosphere. Journal, 02 (02), 57–63.
• Ťapák, P. and Huba, M., (2018). One Degree of Freedom Copter. In: Moreno-Díaz, R., Pichler, F., and Quesada-Arencibia, A., eds. Computer Aided Systems Theory – EUROCAST 2017. Cham: Springer International Publishing, 91–98.
• Tugal, H., Okumuş, H. I. and Tugal, H., (2010). Bir Hava Isıtma Sisteminin Optimum Modellenmesi. In: Electrical, Electronics and Computer Engineering [online]. Available from: https://www.researchgate.net/publication/251987598.
• Wei, W., Tischler, M. B. and Cohen, K., (2017). System identification and controller optimization of a quadrotor unmanned aerial vehicle in hover. Journal of the American Helicopter Society, 62 (4).
• Yu, Y., Tang, P., Song, T. and Lin, D., (2020). A two-step method for system identification of low-cost quadrotor. Aerospace Science and Technology [online], 96, 105551. Available from: https://www.sciencedirect.com/science/article/pii/S1270963819309368.
• Yuan, W. and Katupitiya, J., (2011). A time-domain grey-box system identification procedure for scale model helicopters. In: Proceedings of the 2011 Australasian Conference on Robotics and Automation.
• Zaloğlu, M., Fi̇dan, Ş. and Erkan, E., (2023). Meta-Heuristik Optimizasyon Algoritmalarının Sistem Tanımlama Problemine Uygulanması ve Performans Karşılaştırması. In: 2nd International Conference on Engineering, Natural and Social Sciences [online]. Konya. Available from: http://as-proceeding.com/://www.icensos.com/.