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Speed and rotor position estimation for sensorless brushless DC motor drive based on particle filter

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Abstract

This paper introduces a position sensorless operation of a brushless DC (BLDC) motor based on an efficient state estimation algorithm. In the proposed method, the speed and rotor position estimation is performed only by measuring the phase currents and line voltages. Estimated values are used to determine the commutation logic and speed controller. The line voltage difference is employed, which eliminates the need for neutral. The states estimation is based on the particle filter that doesn’t have the limitations and drawbacks of the Extended Kalman Filter. First, there is no assumption for the probability density functions of the measurement and process noise, it works for Gaussian and non-Gaussian noises. Second, it is not based on any linearization and can efficiently estimate the states of nonlinear systems. Moreover, a suitable method is adopted for resampling step, which improves the estimation performance and does not increase the complexity. The simulation and experimental results demonstrate the effectiveness of the proposed sensorless method. The proposed method is more robust than other common techniques like EKF at different sampling times. The experimental results show the possibility of the BLDC motor states estimation with appropriate accuracy in steady-state and dynamic operation.

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The data that support the findings of this study are available from the corresponding author upon reasonable request.

References

  1. Toren M (2022) Comparative analysis of the magnet effects on the permanent magnet BLDC motor performance used in electric vehicles. Electr Eng. https://doi.org/10.1007/s00202-022-01536-1

    Article  Google Scholar 

  2. Kerem A (2021) Design, implementation and speed estimation of three-phase 2 kW out-runner permanent magnet BLDC motor for ultralight electric vehicles’. Electr Eng 103(5):2547–2559. https://doi.org/10.1007/s00202-021-01279-5

    Article  Google Scholar 

  3. Krishnan R (2010) Permanent magnet synchronous and brushless Dc motor drives. CRC Press, USA. https://doi.org/10.1201/9781420014235

    Book  Google Scholar 

  4. Yu CH (2017) A practical sensorless commutation method based on virtual neutral voltage for brushless Dc motor. IEEJ Trans Electr Electron Eng 12(5):770–777. https://doi.org/10.1002/tee.22464

    Article  Google Scholar 

  5. Damodharan P, Vasudevan K (2010) Sensorless brushless Dc motor drive based on the zero-crossing detection of back electromotive force (EMF) from the line voltage difference. IEEE Trans Energy Convers 25(3):661–668. https://doi.org/10.1109/TEC.2010.2041781

    Article  Google Scholar 

  6. Hooshmand M, Nomrehsaz A (2015) Sensor-less brushless Dc motor (BLDC) drive using line voltage difference. Majlesi J Mechatron Syst 4(2):35–42

    Google Scholar 

  7. Zhang X, Wang Y (2011) A novel position-sensorless control method for brushless Dc motors. Energy Convers Manag 52(3):1669–1676. https://doi.org/10.1016/j.enconman.2010.10.030

    Article  Google Scholar 

  8. Yoon Y-H, Kim J-M (2018) Precision control of a sensorless PM BLDC motor using PLL control algorithm. Electr Eng 100(2):1097–1111. https://doi.org/10.1007/s00202-017-0571-x

    Article  Google Scholar 

  9. Song X, Han B, Zheng S et al (2017) High-precision sensorless drive for high-speed BLDC motors based on the virtual third harmonic back-EMF. IEEE Trans Power Electron 33(2):1528–1540. https://doi.org/10.1109/TPEL.2017.2688478

    Article  Google Scholar 

  10. Yao X, Lin H, Zhao J (2018) Line voltage difference integral method of commutation error adjustment for sensorless brushless Dc motor. In: 2018 IEEE applied power electronics conference and exposition (APEC). IEEE. https://doi.org/10.1109/APEC.2018.8341111

  11. Su G-J, McKeever JW (2004) Low-cost sensorless control of brushless Dc motors with improved speed range. IEEE Trans Power Electron 19(2):296–302. https://doi.org/10.1109/TPEL.2003.823174

    Article  Google Scholar 

  12. Kim T-H, Ehsani M (2004) Sensorless control of the BLDC motors from near-zero to high speeds. IEEE Trans Power Electron 19(6):1635–1645. https://doi.org/10.1109/TPEL.2004.836625

    Article  Google Scholar 

  13. Ogasawara S, Akagi H (1991) An approach to position sensorless drive for brushless Dc motors. IEEE Trans Ind Appl 27(5):928–933. https://doi.org/10.1109/IAS.1990.152223

    Article  Google Scholar 

  14. Ertugrul N, Acarnley P (1994) A new algorithm for sensorless operation of permanent magnet motors. IEEE Trans Ind Appl 30(1):126–133. https://doi.org/10.1109/28.273630

    Article  Google Scholar 

  15. Acarnley PP, Watson JF (2006) Review of position-sensorless operation of brushless permanent-magnet machines. IEEE Trans Ind Electron 53(2):352–362. https://doi.org/10.1109/TIE.2006.870868

    Article  Google Scholar 

  16. Terzic B, Jadric M (2001) Design and implementation of the extended Kalman filter for the speed and rotor position estimation of brushless Dc motor. IEEE Trans Ind Electron 48(6):1065–1073. https://doi.org/10.1109/41.969385

    Article  Google Scholar 

  17. Lenine D, Reddy BR, Kumar SV (2007) Estimation of speed and rotor position of BLDC motor using extended Kalman filter. Int Conf Inf Commun Technol Electr Sci. https://doi.org/10.1049/ic:20070652

    Article  Google Scholar 

  18. Kettle P, Murray A, Moynihan F (1998) Sensorless control of a brushless Dc motor using an extended Kalman estimator. In: PCIM

  19. Vanchinathan K et al (2022) Numerical simulation and experimental verification of fractional-order PIλ controller for solar PV Fed sensorless brushless DC motor using Whale optimization algorithm. Electr Power Compon Syst 50(1–2):64–80. https://doi.org/10.1080/15325008.2022.2135644

    Article  Google Scholar 

  20. Vanchinathan K, Selvaganesan N (2021) Adaptive fractional order PID controller tuning for brushless DC motor using artificial bee colony algorithm. Results Control Optim 4:100032. https://doi.org/10.1016/j.rico.2021.100032

    Article  Google Scholar 

  21. Vanchinathan K et al (2021) Design methodology and experimental verification of intelligent speed controllers for sensorless permanent magnet brushless DC motor: intelligent speed controllers for electric motor. Int Trans Electr Energy Syst 31(9):e12991. https://doi.org/10.1002/2050-7038.13251

    Article  Google Scholar 

  22. Vanchinathan K et al (2021) An improved incipient whale optimization algorithm based robust fault detection and diagnosis for sensorless brushless DC motor drive under external disturbances. Int Trans Electr Energy Syst 31(12):e13251. https://doi.org/10.1002/2050-7038.12991

    Article  Google Scholar 

  23. Ejlali A, Soleimani J (2012) Sensorless vector control of 3-phase BLDC motor using a novel extended Kalman. In: 2012 International conference on advances in power conversion and energy technologies (APCET). IEEE. https://doi.org/10.1109/APCET.2012.6302062

  24. Alex SS, Daniel AE, Jayanand B (2016) Reduced order extended Kalman filter for state estimation of brushless Dc motor. In: 2016 Sixth international symposium on embedded computing and system design (ISED). IEEE. https://doi.org/10.1109/ISED.2016.7977089

  25. Aishwarya V, Jayanand B (2016) Estimation and control of sensorless brushless Dc motor drive using extended Kalman filter. In: 2016 International conference on circuit, power and computing technologies (ICCPCT), IEEE. https://doi.org/10.1109/ICCPCT.2016.7530343

  26. Chen X, Liu G (2019) Sensorless optimal commutation steady speed control method for a nonideal back-EMF BLDC motor drive system including buck converter. IEEE Trans Ind Electron 67(7):6147–6157. https://doi.org/10.1109/TIE.2019.2945282

    Article  Google Scholar 

  27. Ding Z, Wei G, Ding X et al (2015) Scale-corrected minimal skew simplex sampling UKF for BLDCM sensorless control. Syst Sci Control Eng 3(1):340–350. https://doi.org/10.1080/21642583.2015.1023471

    Article  Google Scholar 

  28. Lv H, Wei G, Ding Z et al (2015) Sensorless control for the brushless Dc motor: an unscented Kalman filter algorithm. Syst Sci Control Eng 3(1):8–13. https://doi.org/10.1080/21642583.2014.982769

    Article  Google Scholar 

  29. Jafarboland M, Silabi MHR (2018) New sensorless commutation method for BLDC motors based on the line-to-line flux linkage theory. IET Electr Power Appl 13(6):703–711

    Article  Google Scholar 

  30. Mazaheri A, Radan A (2017) Performance evaluation of nonlinear Kalman filtering techniques in low speed brushless Dc motors driven sensor-less positioning systems. Control Eng Pract 60:148–156. https://doi.org/10.1016/j.conengprac.2017.01.004

    Article  Google Scholar 

  31. Simon D (2006) Optimal state estimation: Kalman, H infinity, and nonlinear approaches. John Wiley & Sons, New York, United States

    Book  Google Scholar 

  32. Ristic B, Arulampalam S, Gordon N (2004) Beyond the Kalman filter. IEEE Aerosp Electron Syst Mag 19:37–38

    Article  MATH  Google Scholar 

  33. Rigatos GG (2009) Particle and Kalman filtering for state estimation and control of Dc motors. ISA Trans 48(1):62–72. https://doi.org/10.1016/j.isatra.2008.10.005

    Article  Google Scholar 

  34. Kim S-H (2017) Electric motor control: DC, AC, and BLDC motors. Elsevier, Amsterdam, Netherlands

    Google Scholar 

  35. Hartikainen J, Solin A, Särkkä S (2011) Optimal filtering with Kalman filters and smoothers. Department of biomedical engineering and computational sciences, Aalto University School of Science

  36. Hooshmand M, Sharifian H, Sharifian MS, Mahmoudi J (2021) HIV virus states estimation by extended Kalman particle filter. In: 2021 29th Iranian conference on electrical engineering (ICEE). IEEE, pp 193–197, https://doi.org/10.1109/ICEE52715.2021.9544254

  37. Kun W, Jie, Tao S (2011) Improvement of fission bootstrap particle filtering. In: IEEE 2011 10th international conference on electronic measurement & instruments. IEEE, pp 94–100, https://doi.org/10.1109/ICEMI.2011.6037863

  38. Sharifian MS, Rahimi A, Pariz N (2016) Classifying the weights of particle filters in nonlinear systems. Commun Nonlinear Sci Numer Simul 31(1–3):69–75. https://doi.org/10.1016/j.cnsns.2015.05.021

    Article  MATH  Google Scholar 

  39. Qiang X, Zhu Y, Xue A (2019) SVRPF: an improved particle filter for a Nonlinear/Non-Gaussian environment. IEEE Access 7:151638–151651. https://doi.org/10.1109/ACCESS.2019.2947540

    Article  Google Scholar 

  40. Gong Z, Gao G, Wang M (2021) An adaptive particle filter for target tracking based on double space-resampling. IEEE Access 9:91053–91061. https://doi.org/10.1109/ACCESS.2019.2947540

    Article  Google Scholar 

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Authors and Affiliations

  1. Faculty of Electrical and Computer Engineering, Semnan University, Semnan, Iran

    Meysam Hooshmand, Hamid Yaghobi & Mostafa Jazaeri

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  1. Meysam Hooshmand
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  2. Hamid Yaghobi
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  3. Mostafa Jazaeri
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Contributions

MH: Conceptualization, Methodology, Software, Investigation, Validation, Writing—original draft. HY: Validation, Formal analysis, Writing—review and editing, Supervision. MJ: Formal analysis, Writing—review and editing, Supervision.

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Correspondence to Hamid Yaghobi.

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Hooshmand, M., Yaghobi, H. & Jazaeri, M. Speed and rotor position estimation for sensorless brushless DC motor drive based on particle filter. Electr Eng 105, 1797–1810 (2023). https://doi.org/10.1007/s00202-023-01773-y

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