SavedAdd your journal

Journals / Journal of Aviation / 2026 / Volume: 9 Issue: 2

Estimating of UAV Battery Status with BSA Based Sugeno Type Fuzzy System

Seda Arık Hatipoğlu(ERCİYES ÜNİVERSİTESİ, HAVACILIK VE UZAY BİLİMLERİ FAKÜLTESİ)0000-0002-6405-9373
Beyzanur Özcan0009-0007-8785-5077
Pages
—
Publication Date
2025-06-28
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The obtained simulation results show that the proposed model has high compatibility with true data and its prediction success is high.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:A hybrid model based on Sugeno type fuzzy system and Back-Tracking Search Optimization Algorithm (BSA) was developed for the estimation of battery status, which is one of the most important parameters affecting the remaining endurance of a rotary wing Unmanned Aerial Vehicle (UAV), in this study.
📊
Key Findings & Data:In addition, it is observed that the model performance is sensitive to the membership function type, number of rules and parameter settings.
💡
Conclusion & Impact:The obtained simulation results show that the proposed model has high compatibility with true data and its prediction success is high.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:UAV · Fuzzy Logic · Sugeno

Abstract

A hybrid model based on Sugeno type fuzzy system and Back-Tracking Search Optimization Algorithm (BSA) was developed for the estimation of battery status, which is one of the most important parameters affecting the remaining endurance of a rotary wing Unmanned Aerial Vehicle (UAV), in this study. In the model, flight altitude, ground speed and current values obtained from the battery were determined as input variables; battery status was used as output variable. The data were normalized and the Sugeno type fuzzy system was modelled with different rule numbers and each model structure was optimized with BSA. The obtained simulation results show that the proposed model has high compatibility with true data and its prediction success is high. In addition, it is observed that the model performance is sensitive to the membership function type, number of rules and parameter settings. In this direction, optimizing Sugeno type fuzzy systems with BSA offers an effective and reliable approach in modelling complex and nonlinear systems such as UAV battery status.

🔖

Cite This Article

Hatipoğlu, S. A., Özcan, B. (2025). Estimating of UAV Battery Status with BSA Based Sugeno Type Fuzzy System. Journal of Aviation, Volume: 9 Issue: 2. https://doi.org/10.30518/jav.1694329

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (24)

  1. Allaire, F. C., Tarbouchi, M., Labonté, G., & Fusina, G. (2009). FPGA implementation of genetic algorithm for UAV real-time path planning. In Unmanned Aircraft Systems: International Symposium On Unmanned Aerial Vehicles, UAV’08 (pp. 495-510). Springer Netherlands.
  2. Arik, S., Turkmen, I. and 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.
  3. Austin, R. (2011). Unmanned aircraft systems: UAVS design, development and deployment. John Wiley & Sons.
  4. Bouhoubeiny, E., Benard, E., Bronz, M., Gavrilovich, I., & Bonnin, V. (2016). Optimal design of long endurance mini UAVs for atmospheric measurements. In 2016 Applied Aerodynamics Research Conference. Royal Aeronautical Society.
  5. Chen, D., Zou, F., Lu, R., & Wang, P. (2017). Learning backtracking search optimisation algorithm and its application. Information Sciences, 376, 71-94.
  6. Chicco, D., Warrens, M. J. and Jurman, G. (2021). The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation. PeerJ Computer Science, 7, e623.
  7. Civicioglu, P. (2013). Backtracking search optimization algorithm for numerical optimization problems. Applied Mathematics and computation, 219(15), 8121-8144.
  8. Duan, H., & Luo, Q. (2014). Adaptive backtracking search algorithm for induction magnetometer optimization. IEEE Transactions on Magnetics, 50(12), 1-6.
  9. El-Fergany, A. (2015). Optimal allocation of multi-type distributed generators using backtracking search optimization algorithm. International Journal of Electrical Power & Energy Systems, 64, 1197-1205.
  10. Ho, D. T., Grøtli, E. I., Sujit, P. B., Johansen, T. A., & Sousa, J. B. (2015). Optimization of wireless sensor network and UAV data acquisition. Journal of Intelligent & Robotic Systems, 78, 159-179.
  11. Jang, J. S. R., Sun, C. T., & Mizutani, E. (1997). Neuro-fuzzy and soft computing-a computational approach to learning and machine intelligence [Book Review]. IEEE Transactions on automatic control, 42(10), 1482-1484.
  12. Karaburun, N. N., Hatipoğlu, S. A., & Konar, M. (2024). SOC estimation of Li-Po battery using machine learning and deep learning methods. Journal of Aviation, 8(1), 26-31.
  13. 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.
  14. Konar, M. (2018). Determination of UAVs Thrust System Parameters by Artificial Bee Colony Algorithm. 4th International Conference on Engineering and Natural Sciences (ICENS 2018) (pp.36-40). Kiew, Ukraine.
  15. Konar, M. (2020). Simultaneous determination of maximum acceleration and endurance of morphing UAV with ABC algorithm-based model. Aircraft Engineering and Aerospace Technology, 92(4), 579-586.
  16. Konar, M., & Hatipoğlu, S. A. (2024). Maximisation the autonomous flight performance of unmanned helicopter using BSO algorithm. The Aeronautical Journal, 128(1329), 2656-2667.
  17. Konar, M., Hatipoğlu, S. A., & Akpınar, M. (2024). Improvement of UAV thrust using the BSO algorithm-based ANFIS model. The Aeronautical Journal, 128(1328), 2364-2373.
  18. Luo, Y., & Chen, X. (2012). Data acquisition and communication system of miniature UAV based on dsp. In 2012 International Conference on Control Engineering and Communication Technology (pp. 736-740). IEEE.
  19. Ozkat, E. C., Bektas, O., Nielsen, M. J., & la Cour-Harbo, A. (2023). A data-driven predictive maintenance model to estimate RUL in a multi-rotor UAS. International Journal of Micro Air Vehicles, 15, 17568293221150171.
  20. Stansbury, R. S., Vyas, M. A., & Wilson, T. A. (2009). A survey of UAS technologies for command, control, and communication (C3). In Unmanned Aircraft Systems: International Symposium On Unmanned Aerial Vehicles, UAV’08 (pp. 61-78). Springer Netherlands.
  21. Sugeno, M., & Kang, G. T. (1988). Structure identification of fuzzy model. Fuzzy sets and systems, 28(1), 15-33.
  22. Takagi, T., & Sugeno, M. (1985). Fuzzy identification of systems and its applications to modeling and control. IEEE transactions on systems, man, and cybernetics, (1), 116-132.
  23. Wang, X. J., Liu, S. Y., & Tian, W. K. (2014). Improved backtracking search optimization algorithm with new effective mutation scale factor and greedy crossover strategy. Journal of Computer Applications, 34(9), 2543-2546.
  24. Zhang, C., Lin, Q., Gao, L., & Li, X. (2015). Backtracking search algorithm with three constraint handling methods for constrained optimization problems. Expert Systems with Applications, 42(21), 7831-7845.