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Anfis Based Thrust Estimation of a Small Rotary Wing Drone

Yıl 2020, Sayı: 18, 738 - 742, 15.04.2020

Hüseyin Şahin , Tugrul Oktay , Mehmet Konar

https://doi.org/10.31590/ejosat.694721
Cited By: 2

Öz

Unmanned Aerial Vehicles (UAV) has an increasingly application for military and civilian fields. Currently, UAVs can perform many tasks such as search-rescue, surveillance by safely. UAVs are specifically designed for their using purpose. The designs of UAVs are an important and long process. It is necessary to evaluate many parameters in the thrust system design. Because of thrust system is the most important system of UAVs. Traditional thrust system design is a trial and error method that is costly and ineffective. In this study, we examined that uav which use brushless motor in thrust system. The force generated by the thrust system has been estimated by using the Adaptive Neuro-Fuzzy Inference System (ANFIS). In the ANFIS model, the thrust force estimation was made by using propeller and motor information. RCbenchmark 1580 model dynamometer was used to measure the accuracy of the ANFIS estimates. Mean square error (MSE) was used to compare test data and ANFIS model. Low MSE ratio shows that ANFIS model is near to real data.

Anahtar Kelimeler

Electric UAV Thrust ANFIS

Kaynakça

  • Barton, J. D. (2012). Fundamentals of Small Unmanned Aircraft Flight. Johns Hopkins APL Technical Digest (Applied Physics Laboratory), 31(2), 132–149.
  • Hobbs, A. (2010). Unmanned Aircraft Systems. In Human Factors in Aviation (pp. 505–531). Elsevier Inc. https://doi.org/10.1016/B978-0-12-374518-7.00016-X
  • Nonami K., Kendoul F., Suzuki S., Wang W., N. D. (2010). Fundamental Modeling and Control of Small and Miniature Unmanned Helicopters. In: Autonomous Flying Robots. In Autonomous Flying Robots: Unmanned Aerial Vehicles and Micro\nAerial Vehicles. https://doi.org/10.1007/978-4-431-53856-1
  • Sahin, H., & Oktay, T., & Konar, M., (2020). İnsansız Hava Aracı İtki Gücünün Tahmini. Uluslararası 5 Ocak Uygulamalı Bilimler Kongresi, Adana, Türkiye, 3 - 05 January 2020
  • McDonald, R. A. (2014). Electric Propulsion Modeling for Conceptual Aircraft Design. 52nd Aerospace Sciences Meeting. https://doi.org/10.2514/6.2014-0536
  • Gohardani, A. S., Doulgeris, G., & Singh, R. (2011, July). Challenges of Future Aircraft Propulsion: A Review of Distributed Propulsion Technology and Its Potential Application for the All Electric Commercial Aircraft. Progress in Aerospace Sciences.
  • Sahin, H., & Oktay, T. (2017). Powerplant System Design for Unmanned Tricopter. The Eurasia Proceedings of Science, Technology, Engineering & Mathematics, 1, 9–21.
  • Avanzini, G., de Angelis, E. L., & Giulietti, F. (2016). Optimal Performance and Sizing of a Battery-powered Aircraft. Aerospace Science and Technology. https://doi.org/10.1016/j.ast.2016.10.015
  • Austin, R. (2010). Unmanned Aircraft Systems: UAVS Design, Development and Deployment. In Unmanned Aircraft Systems: UAVS Design, Development and Deployment. https://doi.org/10.1002/9780470664797
  • Chang, T., & Yu, H. (2015). Improving Electric Powered UAVs’ Endurance by Incorporating Battery Dumping Concept. Procedia Engineering. https://doi.org/10.1016/j.proeng.2014.12.522
  • Valavanis, K. P., & Vachtsevanos, G. J. (2015). Handbook of Unmanned Aerial Vehicles (pp. 1–3022). Springer Netherlands. https://doi.org/10.1007/978-90-481-9707-1
  • Konar, M. (2019). GAO Algoritma Tabanlı YSA Modeliyle İHA Motorunun Performansının ve Uçuş Süresinin Maksimizasyonu. European Journal of Science and Technology, 15, 360–367. https://doi.org/10.31590/ejosat.529093
  • Güler, I., & Übeyli, E. D. (2005). Adaptive Neuro-Fuzzy Inference System for Classification of EEG Signals Using Wavelet Coefficients. Journal of Neuroscience Methods. https://doi.org/10.1016/j.jneumeth.2005.04.013
  • Doğan, O. (2016). Uyarlamalı Sinirsel Bulanık Çıkarım Sisteminin (ANFIS) Talep Tahmini İçin Kullanımı ve Bir Uygulama. Dokuz Eylül Üniversitesi İktisadi ve İdari Bilimler Dergisi. https://doi.org/10.24988/deuiibf.2016311513
  • Jang, J. S. R. (1993). ANFIS: Adaptive-Network-Based Fuzzy Inference System. IEEE Transactions on Systems, Man and Cybernetics. https://doi.org/10.1109/21.256541
  • Konar, M., & Bagiş, A. (2009). Uçuş Kontrol Sistemi Hız Parametresinin Adaptif Aǧ Yapılı Bulanık Sonuç Çıkarım Sistemi Kullanılarak Belirlenmesi. 2009 IEEE 17th Signal Processing and Communications Applications Conference, SIU 2009. https://doi.org/10.1109/SIU.2009.5136565

Anfis Based Thrust Estimation of a Small Rotary Wing Drone

Yıl 2020, Sayı: 18, 738 - 742, 15.04.2020

Hüseyin Şahin , Tugrul Oktay , Mehmet Konar

https://doi.org/10.31590/ejosat.694721
Cited By: 2

Öz

Unmanned Aerial Vehicles (UAV) has an increasingly application for military and civilian fields. Currently, UAVs can perform many tasks such as search-rescue, surveillance by safely. UAVs are specifically designed for their using purpose. The designs of UAVs are an important and long process. It is necessary to evaluate many parameters in the thrust system design. Because of thrust system is the most important system of UAVs. Traditional thrust system design is a trial and error method that is costly and ineffective. In this study, we examined that uav which use brushless motor in thrust system. The force generated by the thrust system has been estimated by using the Adaptive Neuro-Fuzzy Inference System (ANFIS). In the ANFIS model, the thrust force estimation was made by using propeller and motor information. RCbenchmark 1580 model dynamometer was used to measure the accuracy of the ANFIS estimates. Mean square error (MSE) was used to compare test data and ANFIS model. Low MSE ratio shows that ANFIS model is near to real data.

Anahtar Kelimeler

Electric UAV Thrust ANFIS

Kaynakça

  • Barton, J. D. (2012). Fundamentals of Small Unmanned Aircraft Flight. Johns Hopkins APL Technical Digest (Applied Physics Laboratory), 31(2), 132–149.
  • Hobbs, A. (2010). Unmanned Aircraft Systems. In Human Factors in Aviation (pp. 505–531). Elsevier Inc. https://doi.org/10.1016/B978-0-12-374518-7.00016-X
  • Nonami K., Kendoul F., Suzuki S., Wang W., N. D. (2010). Fundamental Modeling and Control of Small and Miniature Unmanned Helicopters. In: Autonomous Flying Robots. In Autonomous Flying Robots: Unmanned Aerial Vehicles and Micro\nAerial Vehicles. https://doi.org/10.1007/978-4-431-53856-1
  • Sahin, H., & Oktay, T., & Konar, M., (2020). İnsansız Hava Aracı İtki Gücünün Tahmini. Uluslararası 5 Ocak Uygulamalı Bilimler Kongresi, Adana, Türkiye, 3 - 05 January 2020
  • McDonald, R. A. (2014). Electric Propulsion Modeling for Conceptual Aircraft Design. 52nd Aerospace Sciences Meeting. https://doi.org/10.2514/6.2014-0536
  • Gohardani, A. S., Doulgeris, G., & Singh, R. (2011, July). Challenges of Future Aircraft Propulsion: A Review of Distributed Propulsion Technology and Its Potential Application for the All Electric Commercial Aircraft. Progress in Aerospace Sciences.
  • Sahin, H., & Oktay, T. (2017). Powerplant System Design for Unmanned Tricopter. The Eurasia Proceedings of Science, Technology, Engineering & Mathematics, 1, 9–21.
  • Avanzini, G., de Angelis, E. L., & Giulietti, F. (2016). Optimal Performance and Sizing of a Battery-powered Aircraft. Aerospace Science and Technology. https://doi.org/10.1016/j.ast.2016.10.015
  • Austin, R. (2010). Unmanned Aircraft Systems: UAVS Design, Development and Deployment. In Unmanned Aircraft Systems: UAVS Design, Development and Deployment. https://doi.org/10.1002/9780470664797
  • Chang, T., & Yu, H. (2015). Improving Electric Powered UAVs’ Endurance by Incorporating Battery Dumping Concept. Procedia Engineering. https://doi.org/10.1016/j.proeng.2014.12.522
  • Valavanis, K. P., & Vachtsevanos, G. J. (2015). Handbook of Unmanned Aerial Vehicles (pp. 1–3022). Springer Netherlands. https://doi.org/10.1007/978-90-481-9707-1
  • Konar, M. (2019). GAO Algoritma Tabanlı YSA Modeliyle İHA Motorunun Performansının ve Uçuş Süresinin Maksimizasyonu. European Journal of Science and Technology, 15, 360–367. https://doi.org/10.31590/ejosat.529093
  • Güler, I., & Übeyli, E. D. (2005). Adaptive Neuro-Fuzzy Inference System for Classification of EEG Signals Using Wavelet Coefficients. Journal of Neuroscience Methods. https://doi.org/10.1016/j.jneumeth.2005.04.013
  • Doğan, O. (2016). Uyarlamalı Sinirsel Bulanık Çıkarım Sisteminin (ANFIS) Talep Tahmini İçin Kullanımı ve Bir Uygulama. Dokuz Eylül Üniversitesi İktisadi ve İdari Bilimler Dergisi. https://doi.org/10.24988/deuiibf.2016311513
  • Jang, J. S. R. (1993). ANFIS: Adaptive-Network-Based Fuzzy Inference System. IEEE Transactions on Systems, Man and Cybernetics. https://doi.org/10.1109/21.256541
  • Konar, M., & Bagiş, A. (2009). Uçuş Kontrol Sistemi Hız Parametresinin Adaptif Aǧ Yapılı Bulanık Sonuç Çıkarım Sistemi Kullanılarak Belirlenmesi. 2009 IEEE 17th Signal Processing and Communications Applications Conference, SIU 2009. https://doi.org/10.1109/SIU.2009.5136565
Toplam 16 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Mühendislik
Bölüm Makaleler
Yazarlar

Hüseyin Şahin 0000-0003-0464-2644

Tugrul Oktay 0000-0003-4860-2230

Mehmet Konar 0000-0002-9317-1196

Yayımlanma Tarihi 15 Nisan 2020
Yayımlandığı Sayı Yıl 2020 Sayı: 18

Kaynak Göster

APA Şahin, H., Oktay, T., & Konar, M. (2020). Anfis Based Thrust Estimation of a Small Rotary Wing Drone. Avrupa Bilim Ve Teknoloji Dergisi(18), 738-742. https://doi.org/10.31590/ejosat.694721

Cited By

SOC Estimation of Li-Po Battery Using Machine Learning and Deep Learning Methods
Journal of Aviation
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Mamdani Modeli tabanlı ANFIS ve Emisyon Tahmininde Uygulanması
European Journal of Science and Technology
Muhammet ÖZTÜRK
https://doi.org/10.31590/ejosat.783878
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