Research Article
BibTex RIS Cite

The Maximization of the UAV Engine Performance and Flight Time by BSA based ANN model

Year 2019, Issue: 15, 360 - 367, 31.03.2019
https://doi.org/10.31590/ejosat.529093

Abstract

Unmanned aerial vehicles
(UAVs) are aircraft that designed to carry out the desired tasks. In the design
of these aircraft, the maximization of engine performance and flight time is
very important. In this study, the maximization of the UAV's brushless motor
performance and flight time are discussed. For this purpose, a model based on
Artificial Neural Networks (ANN) depending on Back-Tracking Search Optimization
(BSO) algorithm is proposed. In the proposed model, four parameters including
the signal of electronic speed control (ESC) unit that adjusts the brushless
engine speed, the number of rotations of the brushless motor (RPM) per minute,
the propeller size, and the propeller pitch were selected as input parameters.
The required thrust, flight time and engine efficiency were selected as output
parameters. Thus, a model with 4 inputs and 3 outputs was formed. To use the in
the training process of the proposed model, data was obtained from the
brushless motor with the help of RCBenchmark's 1580 model dynamometer. By using
these produced data, the parameters of the optimum ANN structure were
determined by BSO algorithm. The ANN structure that optimally determined was
integrated with the BSO algorithm to achieve the objective function. With this
integration, the model based on the BSO algorithm provided the values of input
parameters for maximum engine performance and maximum flight time. That is, the
BSO algorithm was used both to optimize the ANN structure and to obtain the
parameters required for maximization of brushless engine performance and flight
time. The results of the study were presented by tables and figures. The
results obtained in the simulation process with the BSO Algorithm based ANN
model showed that the proposed method will be facilitated the UAV design for designers.

References

  • Austin, R. (2011). Unmanned aircraft systems: UAVS design, development and deployment (Vol. 54). John Wiley & Sons.
  • Daniel, P. R. (1992). Aircraft design: a conceptual approach. Published by American Institute of Aeronautics and Astronautics Inc.
  • Oktay, T., Arik, S., Turkmen, I., Uzun, M., & Celik, H. (2018). Neural network based redesign of morphing UAV for simultaneous improvement of roll stability and maximum lift/drag ratio. Aircraft Engineering and Aerospace Technology, 90(8), 1203-1212.
  • Arik, S., Turkmen, I., & Oktay, T. (2018). Redesign of morphing UAV for simultaneous improvement of directional stability and maximum lift/drag ratio. Advances in Electrical and Computer Engineering, 18(4), 57-62.
  • Konar, M. (2018). Determination of UAVs thrust system parameters by artificial bee colony algorithm. ICENS 4th International Conference on Engineering and Natural Science, Kiev, Ukraine.
  • Traub, L. W. (2011). Range and endurance estimates for battery-powered aircraft. Journal of Aircraft, 48(2), 703-707.
  • Gur, O., & Rosen, A. (2009). Optimizing electric propulsion systems for unmanned aerial vehicles. Journal of Aircraft, 46(4), 1340-1353.
  • Lawrence, D., & Mohseni, K. (2005). Efficiency analysis for long duration electric MAVs. In Infotech@ Aerospace Conferences, Arlington, Virginia.
  • Avanzini, G., & Giulietti, F. (2013). Maximum range for battery-powered aircraft. Journal of Aircraft, 50(1), 304-307.
  • Elkhedim, B., Benard, E., Bronz, M., Gavrilovic, N., & Bonnin, V. (2016). Optimal design of long endurance mini UAVs for atmospheric measurement. Applied Aerodynamics Conference, Bristol, United Kingdom.
  • Hepperle, M. (2012). Electric flight-potential and limitations. Energy Efficient Technologies and Concepts of Operation, Lisbon, Portugal.
  • Chang, T., & Yu, H. (2015). Improving electric powered UAVs’ endurance by incorporating battery dumping concept. Procedia Engineering, 99, 168-179.
  • Civicioglu, P. (2013). Backtracking search optimization algorithm for numerical optimization problems. Applied Mathematics and Computation, 219(15), 8121-8144.
  • Civicioglu, P., & Besdok, E. (2018). A+ Evolutionary search algorithm and QR decomposition based rotation invariant crossover operator. Expert Systems with Applications, 103, 49-62.
  • Haykin, S., Neural networks-a comprehensive foundation, 2nd ed., Prentice Hall, 1999.
  • Bağiş A., & Konar M. (2010). Uçuş kontrol sistemi yakıt parametresinin yapay sinir ağları kullanılarak belirlenmesi. Akıllı Sistemlerde Yenilikler ve Uygulamaları Sempozyumu (ASYU’2010), Kayseri, Türkiye, 104-108.
  • Konar, M., & Bagiş, A. (2016). Simultaneous computation of the speed and fuel parameters of flight control system by using Anfis and artificial neural networks. 24th Signal Processing and Communication Application Conference (SIU 2016), 1389-1392.
  • Bagis, A., & Konar, M. (2016). Comparison of Sugeno and Mamdani fuzzy models optimized by artificial bee colony algorithm for nonlinear system modelling. Transactions of the Institute of Measurement and Control, 38(5), 579-592.
  • Konar, M., & Bagis, A. (2016). Performance comparison of particle swarm optimization, differential evolution and artificial bee colony algorithms for fuzzy modelling of nonlinear systems. Elektronika ir Elektrotechnika, 22(5), 8-13.

GAO Algoritma tabanlı YSA modeliyle İHA motorunun performansının ve uçuş süresinin maksimizasyonu

Year 2019, Issue: 15, 360 - 367, 31.03.2019
https://doi.org/10.31590/ejosat.529093

Abstract

İnsansız hava araçları (İHA),
istenen görevleri yerine getirebilme kabiliyetine sahip olarak tasarlanmış hava
araçlarıdır. Bu hava araçlarının tasarımında, motor performansı ve uçuş
süresinin maksimizasyonu büyük önem arz etmektedir. Bu çalışmada, İHA’nın
fırçasız motorunun performansının ve uçuş süresinin maksimizasyonu ele
alınmıştır. Bu amaçla, Geri-İzleme Arama Optimizasyon (GAO) algoritması tabanlı
Yapay Sinir Ağlarına (YSA) dayanan bir model önerilmiştir. Önerilen modelde,
fırçasız motor hızını ayarlayan elektronik hız kontrol (EHK) ünitesi sinyali,
fırçasız motorun dakikadaki dönüş sayısı (DDS), pervanenin çapı ve hatvesi giriş
parametreleri olarak belirlenmiş; gerekli itki kuvveti, uçuş süresi ve motor
verimliliği çıkış parametreleri olarak belirlenmiştir. Böylece, 4 giriş 3
çıkışa sahip bir model oluşturulmuştur. Önerilen modelin eğitim işleminde
kullanmak için, fırçasız motordan RCbenchmark firmasının 1580 modeli
dinamometresi yardımıyla, farklı değer aralıklarında veriler üretilmiştir.
Üretilen bu veriler kullanılarak optimum YSA yapısına ait parametreler GAO
algoritması ile belirlenmiştir. Optimum olarak belirlenen bu YSA yapısı amaç
fonksiyonunu gerçekleştirmek üzere GAO algoritması ile entegre edilmiştir. Bu
entegrasyonla birlikte GAO algoritmasına dayanan model ile maksimum motor
performansı ve uçuş süresi için giriş parametre değerlerinin elde edilmesi
sağlanmıştır. Yani, GAO algoritması hem YSA yapısının optimizasyonu hem de
fırçasız motor performansı ve uçuş süresinin maksimizasyonu için gerekli
parametrelerin elde edilmesi için kullanılmıştır. Çalışma sonucunda elde edilen
sonuçlar tablolar ve şekiller vasıtasıyla sunulmuştur. GAO algoritması tabanlı
YSA modeli ile yapılan simülasyon çalışmalarında elde edilen sonuçlar, önerilen
yöntemin İHA tasarımcıları için kolaylık sağlayacağını göstermiştir.

References

  • Austin, R. (2011). Unmanned aircraft systems: UAVS design, development and deployment (Vol. 54). John Wiley & Sons.
  • Daniel, P. R. (1992). Aircraft design: a conceptual approach. Published by American Institute of Aeronautics and Astronautics Inc.
  • Oktay, T., Arik, S., Turkmen, I., Uzun, M., & Celik, H. (2018). Neural network based redesign of morphing UAV for simultaneous improvement of roll stability and maximum lift/drag ratio. Aircraft Engineering and Aerospace Technology, 90(8), 1203-1212.
  • Arik, S., Turkmen, I., & Oktay, T. (2018). Redesign of morphing UAV for simultaneous improvement of directional stability and maximum lift/drag ratio. Advances in Electrical and Computer Engineering, 18(4), 57-62.
  • Konar, M. (2018). Determination of UAVs thrust system parameters by artificial bee colony algorithm. ICENS 4th International Conference on Engineering and Natural Science, Kiev, Ukraine.
  • Traub, L. W. (2011). Range and endurance estimates for battery-powered aircraft. Journal of Aircraft, 48(2), 703-707.
  • Gur, O., & Rosen, A. (2009). Optimizing electric propulsion systems for unmanned aerial vehicles. Journal of Aircraft, 46(4), 1340-1353.
  • Lawrence, D., & Mohseni, K. (2005). Efficiency analysis for long duration electric MAVs. In Infotech@ Aerospace Conferences, Arlington, Virginia.
  • Avanzini, G., & Giulietti, F. (2013). Maximum range for battery-powered aircraft. Journal of Aircraft, 50(1), 304-307.
  • Elkhedim, B., Benard, E., Bronz, M., Gavrilovic, N., & Bonnin, V. (2016). Optimal design of long endurance mini UAVs for atmospheric measurement. Applied Aerodynamics Conference, Bristol, United Kingdom.
  • Hepperle, M. (2012). Electric flight-potential and limitations. Energy Efficient Technologies and Concepts of Operation, Lisbon, Portugal.
  • Chang, T., & Yu, H. (2015). Improving electric powered UAVs’ endurance by incorporating battery dumping concept. Procedia Engineering, 99, 168-179.
  • Civicioglu, P. (2013). Backtracking search optimization algorithm for numerical optimization problems. Applied Mathematics and Computation, 219(15), 8121-8144.
  • Civicioglu, P., & Besdok, E. (2018). A+ Evolutionary search algorithm and QR decomposition based rotation invariant crossover operator. Expert Systems with Applications, 103, 49-62.
  • Haykin, S., Neural networks-a comprehensive foundation, 2nd ed., Prentice Hall, 1999.
  • Bağiş A., & Konar M. (2010). Uçuş kontrol sistemi yakıt parametresinin yapay sinir ağları kullanılarak belirlenmesi. Akıllı Sistemlerde Yenilikler ve Uygulamaları Sempozyumu (ASYU’2010), Kayseri, Türkiye, 104-108.
  • Konar, M., & Bagiş, A. (2016). Simultaneous computation of the speed and fuel parameters of flight control system by using Anfis and artificial neural networks. 24th Signal Processing and Communication Application Conference (SIU 2016), 1389-1392.
  • Bagis, A., & Konar, M. (2016). Comparison of Sugeno and Mamdani fuzzy models optimized by artificial bee colony algorithm for nonlinear system modelling. Transactions of the Institute of Measurement and Control, 38(5), 579-592.
  • Konar, M., & Bagis, A. (2016). Performance comparison of particle swarm optimization, differential evolution and artificial bee colony algorithms for fuzzy modelling of nonlinear systems. Elektronika ir Elektrotechnika, 22(5), 8-13.
There are 19 citations in total.

Details

Primary Language Turkish
Subjects Engineering
Journal Section Articles
Authors

Mehmet Konar 0000-0002-9317-1196

Publication Date March 31, 2019
Published in Issue Year 2019 Issue: 15

Cite

APA Konar, M. (2019). GAO Algoritma tabanlı YSA modeliyle İHA motorunun performansının ve uçuş süresinin maksimizasyonu. Avrupa Bilim Ve Teknoloji Dergisi(15), 360-367. https://doi.org/10.31590/ejosat.529093