Research Article
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Deep Neural Network, PID Algoritması ve Başkalaşım ile Hexarotor Boylamasına Uçuş Kontrolü

Year 2021, Issue: 27, 115 - 124, 30.11.2021
https://doi.org/10.31590/ejosat.946884

Abstract

İnsansız Hava Araçları (İHA) günümüzde askeri operasyonlardan eğlenceye kadar yaşamın ayrılmaz bir parçası durumuna gelmşitir. Bu popülerite ile birlikte araştırmacıların da bu İHA’lar üzerine olan ilgisi giderek artmıştır. İHA’lar birçok alanda kullanılması ve araştırmacılar tarafılan yapılan araştırmalar bu cihazların da kısıtlamalarını ortaya çıkarmıştır. Bu kısıtlamaların başında özellikle dar alandan geçiş gelmektedir. Bu çalışmada bir hexarotor İHA’ın boylamasına uçuş kontrolü başkalaşım ile ele alınmıştır. Bu çalışmada ki kısıt ise başkalaşım ile birlikte hexarotor kol uzunluklarında değişim olması ve buna bağlı olarak katı cisim modeli değiştiğinden dolayı atalet momenti ve oransal integral türev(PID) katsayı değerlerinin kol uzunluğuna göre tahmin edilmesinin zor olmasıdır. Bu çalışmada bu sorunun üstesinden gelmek için Derin Sinir Ağı(iki yada daha fazla hidden layer içeren yapay sinir ağı) ile bu parametrelerin elde edilmesi amaçlanmıştır. Hexarotor başkalaşım durumlarına ait 15 adet çizim Solidworks programında çizilmiştir. Bu çizimlere ait boylamasına uçuş için PID katsayıları ise Matlab/Simulink programından elde edilerek bir eğitim seti oluşturulmuştur. Eğitim seti Derin Sinir Ağına öğretilerek rasgele olarak tahmin edilen kol uzama ya da kısalma oranlarına göre atalet momentleri ve PID katsayıları elde edilmiştir. Ayrıca hexarotor dinamik modeli ise Newton-Euler yaklaşımına göre türetilmiş ve durum uzay modeli kullanılarak modellenmiştir. Durum uzay modeli ile hexarotor boylasına uçuş simülasyonu gerçekleştirilmiştir. Başlangıç durumu ile birlikte rasgele olarak program tarafından belirlenen değerlere göre atalet momenti ve PID katsayıları Deep Neural Network ile tahmin edilmiştir. Bu parametreler ile simülasyonlar yapılarak sonuçlar grafikler halinde verilmiştir.

References

  • Alaimo, A., Artale, V., Milazzo, C., Ricciardello, A., & Trefiletti, L. (2013). Mathematical modeling and control of a hexacopter. 2013 International Conference on Unmanned Aircraft Systems (ICUAS), 1043–1050.
  • Ko, W., Oo, K., Tun, H. M., Naing, Z. M., & Moe, W. K. (2017). Design Of Vertical Take-Off And Landing (VTOL) Aircraft System. International Journal of Scientific & Technology Research, 6(4), 179–183.
  • Kose, O., & Oktay, T. (2020a). Effect Of Differential Morphing On Yaw Movement In Quadrotors.
  • Kose, O., & Oktay, T. (2020b). Investigation of the Effect of Differential Morphing on Lateral Flight by Using PID Algorithm in Quadrotors. European Journal of Science and Technology, 18, 636–644. doi: 10.31590/ejosat.702727
  • Kose, O., & Oktay, T. (2020c). The Effect of Differential Morphing on the Hover Flight in Quadcopter. In Bilgisayar Mühendisliği Çalışmaları 1 (p. 59). Iksad.
  • Kose, O., & Oktay, T. (2020d). The Effect of Collective and Differential Morphing on Longitudinal Flight in Quadrotors. N. A. B. BAYRAKTAR (Ed.), 10th. International Conference on Mathematics, Engıneering, Natural and Medical Sciences (pp. 133–142). Batumi, Georgia: Iksad.
  • Le, D.-K., & Nam, T.-K. (2015). A study on the modeling of a hexacopter. Journal of the Korean Society of Marine Engineering, 39(10), 1023–1030. doi: 10.5916/jkosme.2015.39.10.1023
  • Oktay, T., & Köse, O. (2019). Dynamic Modeling and Simulation of Quadrotor for Different Flight Conditions. European Journal of Science and Technology. doi: 10.31590/ejosat.507222
  • Pflimlin, J. M., Soueres, P., & Hamel, & T. (2007). Position control of a ducted fan VTOL UAV in crosswind. International Journal of Control, 80(5), 666–683. doi: 10.1080/00207170601045034
  • Sanca, A. S., Alsina, P. J., & De Jesus F. Cerqueira, J. (2010). Dynamic modeling with nonlinear inputs and backstepping control for a hexarotor micro-aerial vehicle. Proceedings - 2010 Latin American Robotics Symposium and Intelligent Robotics Meeting, LARS 2010, 36–42. doi: 10.1109/LARS.2010.14
  • Wang, L., & Poksawat, P. (2017). Automatic tuning of hexacopter attitude control systems with experimental validation. 2017 21st International Conference on System Theory, Control and Computing, ICSTCC 2017, 753–758. doi: 10.1109/ICSTCC.2017.8107127

Hexarotor Longitudinal Flight Control with Deep Neural Network, PID Algorithm and Morphing

Year 2021, Issue: 27, 115 - 124, 30.11.2021
https://doi.org/10.31590/ejosat.946884

Abstract

Unmanned Aerial Vehicles (UAV) have become an integral part of life, from military operations to entertainment. With this popularity, the interest of researchers on these UAVs has gradually increased. The use of UAVs in many areas and the researches carried out by researchers have revealed the limitations of these devices. At the beginning of these restrictions is the passage through the narrow space. In this study, longitudinal flight control of a hexarotor UAV is discussed with morphing. The limitation of this study is that it is difficult to estimate the moment of inertia and proportional integral derivative (PID) coefficient values according to the arm length, since there is a change in the hexarotor arm lengths with the morphing and the rigid body model changes accordingly. In this study, it is aimed to obtain these parameters with the Deep Neural Network(Artificial Neural Network(ANN) with two or more hidden layers) to overcome this problem. 15 drawings of hexarotor morphing states were drawn in Solidworks program. A training set was created by obtaining the PID coefficients for the longitudinal flight of these drawings from the Matlab/Simulink program. The training set was taught to the Deep Neural Network and moments of inertia and PID coefficients were obtained according to the arbitrarily estimated arm extension or shortening rates. In addition, the hexarotor dynamic model was derived according to the Newton-Euler approach and modeled using the state space model. The longitudinal flight of the hexarotor is simulated with the state space model. The moment of inertia and PID coefficients were estimated by Deep Neural Network according to the values determined by the program randomly together with the initial state. Simulations were made with these parameters and the results were given in graphics.

References

  • Alaimo, A., Artale, V., Milazzo, C., Ricciardello, A., & Trefiletti, L. (2013). Mathematical modeling and control of a hexacopter. 2013 International Conference on Unmanned Aircraft Systems (ICUAS), 1043–1050.
  • Ko, W., Oo, K., Tun, H. M., Naing, Z. M., & Moe, W. K. (2017). Design Of Vertical Take-Off And Landing (VTOL) Aircraft System. International Journal of Scientific & Technology Research, 6(4), 179–183.
  • Kose, O., & Oktay, T. (2020a). Effect Of Differential Morphing On Yaw Movement In Quadrotors.
  • Kose, O., & Oktay, T. (2020b). Investigation of the Effect of Differential Morphing on Lateral Flight by Using PID Algorithm in Quadrotors. European Journal of Science and Technology, 18, 636–644. doi: 10.31590/ejosat.702727
  • Kose, O., & Oktay, T. (2020c). The Effect of Differential Morphing on the Hover Flight in Quadcopter. In Bilgisayar Mühendisliği Çalışmaları 1 (p. 59). Iksad.
  • Kose, O., & Oktay, T. (2020d). The Effect of Collective and Differential Morphing on Longitudinal Flight in Quadrotors. N. A. B. BAYRAKTAR (Ed.), 10th. International Conference on Mathematics, Engıneering, Natural and Medical Sciences (pp. 133–142). Batumi, Georgia: Iksad.
  • Le, D.-K., & Nam, T.-K. (2015). A study on the modeling of a hexacopter. Journal of the Korean Society of Marine Engineering, 39(10), 1023–1030. doi: 10.5916/jkosme.2015.39.10.1023
  • Oktay, T., & Köse, O. (2019). Dynamic Modeling and Simulation of Quadrotor for Different Flight Conditions. European Journal of Science and Technology. doi: 10.31590/ejosat.507222
  • Pflimlin, J. M., Soueres, P., & Hamel, & T. (2007). Position control of a ducted fan VTOL UAV in crosswind. International Journal of Control, 80(5), 666–683. doi: 10.1080/00207170601045034
  • Sanca, A. S., Alsina, P. J., & De Jesus F. Cerqueira, J. (2010). Dynamic modeling with nonlinear inputs and backstepping control for a hexarotor micro-aerial vehicle. Proceedings - 2010 Latin American Robotics Symposium and Intelligent Robotics Meeting, LARS 2010, 36–42. doi: 10.1109/LARS.2010.14
  • Wang, L., & Poksawat, P. (2017). Automatic tuning of hexacopter attitude control systems with experimental validation. 2017 21st International Conference on System Theory, Control and Computing, ICSTCC 2017, 753–758. doi: 10.1109/ICSTCC.2017.8107127
There are 11 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Articles
Authors

Oguz Kose 0000-0002-8069-8749

Tugrul Oktay 0000-0003-4860-2230

Early Pub Date July 29, 2021
Publication Date November 30, 2021
Published in Issue Year 2021 Issue: 27

Cite

APA Kose, O., & Oktay, T. (2021). Hexarotor Longitudinal Flight Control with Deep Neural Network, PID Algorithm and Morphing. Avrupa Bilim Ve Teknoloji Dergisi(27), 115-124. https://doi.org/10.31590/ejosat.946884