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A Parameter Identification Method Based on Forgetting Factor Dynamic Adjustment for PMSM Applied to the Rapid Control of Satellite Attitude

  • Shun Li
  • Xinxiu Zhou
Original Article
  • 2 Downloads

Abstract

In the vacuum environment of space, the attitude of the satellite can be adjusted by PMSM based on the law of conservation of momentum. The adjustment method of satellite attitude is that the PMSM must rotate in opposite direction quickly when the attitude of the satellite needs to be adjusted in positive direction. In order to receive the ground signals effectively or to communicate with other equipment in space reliably, it requires that the PMSM can be co-rotation and reversal frequently. Therefore, before restart, the stator inductances must be recognized quickly to estimate initial rotor position, with which, the PMSM can get large starting torque to guarantee a quick and reliable restart. This paper presents a dynamic correction method for the identification forgetting factor. With the proposed method, the convergence rate of parameter estimation is obviously accelerated, and the fluctuation of parameter estimation is obviously reduced. Finally, the inductances can be identified quickly and accurately. PMSM can achieve fast and steady forward co-rotation and reversal frequently.

Keywords

Satellite attitude adjustment Start and reversal frequently Inductances identification Dynamic correction of the identification forgetting factor Fast convergence of the identification process 

Notes

Acknowledgements

This work was supported by the National Natural Science Foundation of China under Grant 61721091, by the Civil Aerospace Advance Research Project, and by the National Natural Science Foundation of China under Grant 61873020.

References

  1. 1.
    Yuan Q, Zeng Z, Zhao R (2017) Direct stator flux vector control strategy for IPMSM using a fullorder state observer. J Electr Eng Technol 12(1):236–248CrossRefGoogle Scholar
  2. 2.
    Wang S, Fu J, Yang Y (2017) An improved predictive functional control with minimum-order observer for speed control of permanent magnet synchronous motor. J Electr Eng Technol 12(1):272–283CrossRefGoogle Scholar
  3. 3.
    Liu J, Li HW, Deng YG (2018) Torque ripple minimization of PMSM based on robust ILC via adaptive sliding mode control. IEEE Trans Power Electr 33(4):3655–3671CrossRefGoogle Scholar
  4. 4.
    Xia CL, Deng WT, Shi T, Yan Y (2016) Torque ripple minimization of PMSM using parameter optimization based iterative learning control. J Electr Eng Technol 11(2):425–436CrossRefGoogle Scholar
  5. 5.
    Kim IH, Son YI (2017) A modular disturbance observer-based cascade controller for robust speed regulation of PMSM. J Electr Eng Technol 12(4):1663–1674MathSciNetGoogle Scholar
  6. 6.
    Won IK, Hwang JH, Kim DY (2017) Improved FOC of IPMSM using finite-state model predictive current control for EV. J Electr Eng Technol 12(5):1851–1863Google Scholar
  7. 7.
    Türker T, Yanik G, Buyukkeles U (2017) A discrete-time nonlinear robust controller for current regulation in PMSM drives. J Electr Eng Technol 12(4):1537–1547Google Scholar
  8. 8.
    Lee DM (2016) Position estimator employing Kalman filter for PM motors driven with binary-type Hall sensors. J Electr Eng Technol 11(4):931–938CrossRefGoogle Scholar
  9. 9.
    Kamel T, Abdelkader D, Said B (2018) Sliding mode control based DTC of sensorless parallel-connected two five-phase PMSM drive system. J Electr Eng Technol 13(3):1185–1201Google Scholar
  10. 10.
    Cheema MAM, Fletcher JE, Farshadnia M, Xiao D (2017) Combined speed and direct thrust force control of linear permanent-magnet synchronous motors with sensorless speed estimation using a sliding-mode control with integral action. IEEE Trans Ind Electr 64(5):3489–3501CrossRefGoogle Scholar
  11. 11.
    Moon C, Kwon YA (2016) Sensorless speed control of permanent magnet synchronous motor by unscented Kalman filter using various scaling parameters. J Electr Eng Technol 11(2):347–352CrossRefGoogle Scholar
  12. 12.
    Moon C, Kwon YA (2017) Reduced-order unscented Kalman filter for sensorless control of permanent-magnet synchronous motor. J Electr Eng Technol 12(2):683–688CrossRefGoogle Scholar
  13. 13.
    Han DY, Cho Y, Lee KB (2017) Simple sensorless control of interior permanent magnet synchronous motor using PLL based on extended EMF. J Electr Eng Technol 12(2):711–717CrossRefGoogle Scholar
  14. 14.
    Yang JQ, Mao YL, Chen YS (2017) Sensorless control of permanent magnet synchronous motors with compensation for parameter uncertainty. J Electr Eng Technol 12(3):1166–1176CrossRefGoogle Scholar
  15. 15.
    Wang YQ, Ma XY, Peng JZ, Zhang ML (2016) Analysis and solution of PMSM starting under different rotor initial positions. In: 2016 IEEE vehicle power and propulsion conference, pp 1–6Google Scholar
  16. 16.
    Mishuku Y, Hasegawa M (2017) Initial position estimation of small SPMSM for position sensorless control. In: 2017 IEEE 12th international conference on power electronics and drive systems, pp. 14–19Google Scholar
  17. 17.
    Feuersänger S, Pacas M (2014) Initial rotor position detection in synchronous machines using low frequency pulses. In: IECON 2014—40th annual conference of the IEEE industrial electronics society, pp 675–681Google Scholar
  18. 18.
    Jin XH, Ni RG, Chen W (2018) High frequency voltage injection methods and observer design for initial position detection of permanent magnet synchronous machines. IEEE Trans Power Electron 33(9):7971–7979CrossRefGoogle Scholar
  19. 19.
    Lu JD, Liu JL, Hu YH (2017) Three-phase four-switch inverter fed IPMSM initial position estimation based on HF method. In: 2017 IEEE 26th international symposium on industrial electronics, pp 2081–2085Google Scholar
  20. 20.
    Yuanjun B, Guo X (2014) 2014 IEEE conference and expo transportation electrification Asia-Pacific, pp 1–4Google Scholar
  21. 21.
    Huang ZB, You LR, Wang ZD (2014) Sensorless initial rotor position identification for non-salient permanent magnet synchronous motors based on dynamic reluctance difference. IET Power Electron 7(9):2336–2346CrossRefGoogle Scholar
  22. 22.
    Meng GJ, Yu HT, Hu Q (2015) Initial position estimation of permanent magnet synchronous motors based on variation behavior of winding inductances. In: 2015 9th international conference on power electronics and ECCE Asia, pp 1609–1616Google Scholar
  23. 23.
    Kim HW, Moon BH, Park SM (2017) Design of backstepping controller based on online multiparameter estimation for permanent magnet synchronous motor. In: 2017 11th Asian control conference, pp 1194–1198Google Scholar
  24. 24.
    Li C, Chen Z, Yao B (2018) Adaptive robust synchronization control of a dual-linear-motor-driven gantry with rotational dynamics and accurate online parameter estimation. IEEE Trans Ind Inform 14(7):3013–3022CrossRefGoogle Scholar
  25. 25.
    Bhowmick D, Manna M, Chowdhury SK (2016) Online estimation and analysis of equivalent circuit parameters of three phase induction motor using particle swarm optimization. In: 2016 IEEE 7th power india international conference, pp 1–5Google Scholar
  26. 26.
    Lu H, Wang YK, Yuan Y (2017) Online identification for permanent magnet synchronous motor based on recursive fixed memory least square method under steady state. In: 2017 36th Chinese control conference, pp 4824–4829Google Scholar
  27. 27.
    Ghoul Y, Taarit KI, Ksouri M (2016) Online identification of continuous-time systems with multiple-input time delays from sampled data using sequential nonlinear least square method from sampled data. In: 2016 17th international conference on sciences and techniques of automatic control and computer engineering, pp 265–269Google Scholar
  28. 28.
    Nalakath S, Preindl M, Emadi A (2017) Online multi-parameter estimation of interior permanent magnet motor drives with finite control set model predictive control. IET Electric Power Appl 11(5):944–951CrossRefGoogle Scholar
  29. 29.
    Yang SY, Ding DW, Li X (2017) A novel online parameter estimation method for indirect field oriented induction motor drives. IEEE Trans Energy Conver 32(4):1562–1573CrossRefGoogle Scholar
  30. 30.
    Jun HW, Ahn HW, Lee HW (2017) A maximum power control of IPMSM with real-time parameter identification. J Electr Eng Technol 12(1):110–116CrossRefGoogle Scholar

Copyright information

© The Korean Institute of Electrical Engineers 2019

Authors and Affiliations

  1. 1.School of Instrumentation and Optoelectronic EngineeringBeijing University of Aeronautics and AstronauticsBeijingChina

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