Dergiler / Turkish Journal of Engineering / 2026 / Volume: 10 Issue: 4

Voltage Sensorless Control of Reduced-Order Elementary Super-Lift Luo Converter using Extended Kalman Filter

Sayfa
—
Yayın Tarihi
2026-10-16
🏛️Bu makalenin bibliyografik verileri ve tam metin bağlantısı DergiPark üzerinden sağlanmıştır.
DergiPark Kaydı ↗
✨

Yapay Zeka Hızlı Bakış & Çıkarımlar

AI Insight
⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

The obtained results demonstrate that the proposed voltage sensorless control system provides accurate voltage estimation, robust dynamic performance, and reduced sensor dependency, making it a promising solution for high-performance and cost-effective dc/dc converter applications.

📌 3 Maddede Bu Makale

🎯
Amaç & Hipotez:Sensorless control approaches can enhance system reliability while reducing sensor cost in dc/dc converters.
📊
Temel Bulgular & Veri:The voltage sensorless control system achieves high estimation accuracy with mean absolute error values of 0.2082 V (stepwise input voltage variation), 0.2272 V (stepwise output voltage variation), and 0.1620 V (ramp and stepwise input voltage variation) for the three test scenarios, respectively.
💡
Sonuç & Katkı:The obtained results demonstrate that the proposed voltage sensorless control system provides accurate voltage estimation, robust dynamic performance, and reduced sensor dependency, making it a promising solution for high-performance and cost-effective dc/dc converter applications.
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:2082 Verileri
⚙️Değişkenler:Reduced-order elementary super-lift Luo converter · Extended Kalman Filter · Voltage sensorless control

Abstract

Sensorless control approaches can enhance system reliability while reducing sensor cost in dc/dc converters. In this paper, an extended Kalman filter based voltage sensorless control system is proposed to estimate both the input and output voltages of an elementary super-lift Luo converter using a reduced-order model, thereby decreasing the number of sensors. To regulate the output voltage of the elementary super-lift Luo converter, a proportional-integral controller with a reduced number of tuning parameters is designed to minimize steady-state error. The proposed method is evaluated in the MATLAB/Simulink environment under stepwise input/output voltage variations and ramp input voltage variation. The voltage sensorless control system achieves high estimation accuracy with mean absolute error values of 0.2082 V (stepwise input voltage variation), 0.2272 V (stepwise output voltage variation), and 0.1620 V (ramp and stepwise input voltage variation) for the three test scenarios, respectively. In addition, the proportional-integral controller provides a settling time of approximately 55 ms while maintaining negligible steady-state error for all scenarios. The obtained results demonstrate that the proposed voltage sensorless control system provides accurate voltage estimation, robust dynamic performance, and reduced sensor dependency, making it a promising solution for high-performance and cost-effective dc/dc converter applications.

🔖

Bu Makaleye Atıf Yap

Gani, A., Demir, R. (2026). Voltage Sensorless Control of Reduced-Order Elementary Super-Lift Luo Converter using Extended Kalman Filter. Turkish Journal of Engineering, Volume: 10 Issue: 4. https://doi.org/10.31127/tuje.1935755

📄 Tam Metin PDF Okuyucu

📄

PDF görüntüleyici hazırlanıyor...

Kaynakça (29)

  1. Gülbudak, O., & Gökdağ, M. (2022). Performance evaluation of model predictive control method for neutral point clamped inverter. Turkish Journal of Engineering, 6(3), 245-250. https://doi.org/10.31127/tuje.962857.
  2. Keçecioğlu, O.F., Gani, A., & Sekkeli, M., (2021). Improved hybrid intelligent controller design for MPPT of stand-alone PV System. Turkish Journal of Engineering, 5(1), 20-28. https://doi.org/10.31127/tuje.652229.
  3. Gauswami, A., & Shah, H. (2026). A comparative simulation–hardware co-validation framework for SEPIC and full-bridge DC–DC converters in electric vehicle battery charging via dSPACE DS1104. Turkish Journal of Engineering, 10(2), 534-547. https://doi.org/10.31127/tuje.1828170.
  4. Gani, A., (2025). Advanced controller design based on interval type-2 fuzzy neural network for elementary super-lift Luo converter. Transactions of the Institute of Measurement and Control, 47(10), 1953-1963.https://doi.org/10.1177/0142331224127576.
  5. Gani, A., (2021). Improving dynamic efficiency of photovoltaic generation systems using adaptive type-2 fuzzy neural network via EN 50530 test procedure. International Journal of Circuit Theory and Applications, 49(11), 3922–3940. https://doi.org/10.1002/cta.3139.
  6. Gani, A., (2023). PD-type-2 fuzzy neural network based control of a super-lift Luo converter designed for sustainable future energy applications. In: Kahraman, C., Sari, I.U., Oztaysi, B., Cebi, S., Cevik Onar, S., Tolga, A.Ç. (eds) Intelligent and Fuzzy Systems. INFUS 2023. Lecture Notes in Networks and Systems, vol 758. Springer, Cham.https://doi.org/10.1007/978-3-031-39774-5_62.
  7. Gani, A., (2023). PEM yakıt hücresi ömrünü uzatmaya yönelik temel seri pozitif süper gerilim yükseltmeli Luo çevirici tasarımı. Erciyes Üniversitesi Fen Bilimleri Enstitüsü Fen Bilimleri Dergisi, 39(3), 403-416. https://izlik.org/JA84NU86LC.
  8. Toren, M., Kaba, B., & Öztürk, G., (2025). Performance comparison of boost and super-lift Luo converters in solar energy systems using fuzzy logic based MPPT control. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, https://doi.org/10.65206/pajes.92724.
  9. Garg, M.M., Pathak, M.K., & Behera, L., Dynamic modeling of a non-ideal DC-DC positive output elementary super-lift Luo converter, International Conference on Technological Advancements in Power and Energy (TAP Energy), Kollam, India, 2017, pp. 1-6, https://doi.org/10.1109/TAPENERGY.2017.8397296.
  10. Kaba, B., Tören, M., & Şahin, M.E., (2026). Investigation of the super-lift Luo converter controlled with PI and SM control for electric vehicle battery sources. Journal of Engineering Research.14(2),1782-1791. https://doi.org/10.1016/j.jer.2026.01.008.
  11. Kamarudin, M. R., Mispan, M. S., Zainudin, M. N. S., & Sofian, H., (2026). Reservoir spiking neural networks for accurate state-of-charge estimation in battery management systems. Turkish Journal of Engineering, 10(2), 407-417. https://doi.org/10.31127/tuje.1804828.
  12. Chaudhari, T., (2026). State of charge estimation of sustainable low emission electrochemical energy storage systems. Turkish Journal of Engineering, 10(2), 689-706. https://doi.org/10.31127/tuje.1841528.
  13. Bulut, Y. (2011). Applied Kalman filter theory (Doctoral dissertation, Northeastern University). Northeastern University Repository.
  14. Candan, M. Y., & Ankarali, M.M. (2020). Extended Kalman filter based state and parameter estimation method for a buck converter operating in a wide load range. In 2020 IEEE Energy Conversion Congress and Exposition (ECCE) (pp. 3238–3244). IEEE. https://doi.org/10.1109/ECCE44975.2020.9235695.
  15. Haadi, K., Rajaei, A., Shahparasti, M., & Rahideh, A. (2020). Sensorless voltage observer for a current-fed high step-up DC–DC converter using extended Kalman filter. Electronics, 9(12), 2066. https://doi.org/10.3390/electronics9122066.
  16. Candan, M. Y., & Ankarali, M. M. (2023). Steady-state hybrid extended Kalman filter based sensorless voltage controller for a phase-shifted full-bridge converter. IET Power Electronics, 16(1),53–63. https://doi.org/10.1049/pel2.12362.
  17. Eker, K., Çetinceviz, Y., Şehirli, E., & Erken, F. (2022). State estimation of buck converter by using Kalman filter. In 2022 Global Energy Conference (GEC) (pp. 402–407). IEEE. https://doi.org/10.1109/GEC55014.2022.9986519.
  18. Zhang, Q., Tong, Q., & Zhang, H. (2014). An inductor current observer based on improved EKF for DC/DC converter. In International Symposium on Computer Consumer and Control (pp.892–895).IEEE. https://doi.org/10.1109/IS3C.2014.235.
  19. Cimini, G., Ippoliti, G., Orlando, G., Longhi, S., & Miceli, R. (2017). A unified observer for robust sensorless control of DC–DC converters. Control Engineering Practice, 61, 21–27. https://doi.org/10.1016/j.conengprac.2017.01.012.
  20. Tong, Q., Chen, C., Zhang, Q., & Zou, X. (2015). A sensorless predictive current controlled boost converter by using an EKF with load variation effect elimination function. Sensors, 15(5), 9986–10003. https://doi.org/10.3390/s150509986.
  21. Ahmeid, M., Armstrong, M., Gadoue, S., Al-Greer, M., & Missailidis, P. (2017). Real-time parameter estimation of DC–DC converters using a self-tuned Kalman filter. IEEE Transactions on Power Electronics, 32(7), 5666–5674. https://doi.org/10.1109/TPEL.2016.2606417.
  22. Luo, F. L., & Ye, H. (2003). Positive output super-lift converters. IEEE Transactions on Power Electronics, 18(1), 105–113. https://doi.org/10.1109/TPEL.2002.807198.
  23. Gani, A., & Uçar, İ. (2024). Enhanced Control of polymer electrolyte membrane fuel cell fed Basic series positive super voltage lift Luo converter using interval type-2 fuzzy logic. Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım Ve Teknoloji, 12(2), 457-467. https://doi.org/10.29109/gujsc.1433379.
  24. Luo, F. L., Ye, H., & Rashid, M., (2002). Super-lift Luo-converters. In Proceedings of the IEEE 33rd Annual Power Electronics Specialists Conference (Vol. 2, pp. 425–430). IEEE. https://doi.org/10.1109/PSEC.2002.1022490.
  25. Luo, F.L., & Ye,H. (2003).Advanced DC/DC converters. CRC Press. https://doi.org/10.1201/9780203492925.
  26. Femi, R., Sree Renga Raja, T., & Shenbagalakshmi, R. (2021). A positive output-super lift Luo converter fed brushless DC motor drive using alternative energy sources. International Transactions on Electrical Energy Systems, 31(2), e12740.https://doi.org/10.1002/2050-7038.12740.
  27. Glady, J. B. P., & Manjunath, K. (2019). Analysis-intelligent design and simulation of improved state-space modelling for control of Luo converter. Computers & Electrical Engineering, 75,230–244. https://doi.org/10.1016/j.compeleceng.2017.11.010.
  28. Demir, R., (2023). Robust stator flux and load torque estimations for induction motor drives with EKF-based observer. Electrical Engineering, 105(1), 551–562. https://doi.org/10.1007/s00202-022-01717-y.
  29. Yildiz R., Demir R., and Barut M., (2023).Online estimations for electrical and mechanical parameters of the induction motor by extended Kalman filter, Transactions of the Institute of Measurement and Control 45(14), 2725-2738, https://doi.org/10.1177/01423312231160582.