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Anti load disturbance method for AC servo motor power system

  • Haoliang Lv
  • Xiaojun Zhou
Article
  • 48 Downloads

Abstract

A dimensionality reduction load torque observed is designed to provide compensation control for the motor with the observation value of load torque converted over for the problem that load disturbance decreases the control precision of servo system. A three-loop mathematical model is established for the servo control system. Use of two-/three-order optimal model theory contributed to the derivation of the tuning formula for three-loop control parameters. A simulation model is established for the space vector control of AC servo system. The simulation experiment validates that the tuned three-loop parameter provides the system with good dynamic performance. An experiment platform is built. The load sudden change experiment of the motor demonstrates the feasibility that torque compensation control improves the control precision of servo system in the presence of load disturbance.

Keywords

Servo system Load Anti-inference Motor Parameter tuning 

Notes

Acknowledgement

The Natural Science Foundation of China under Grant No. 51275453.

References

  1. 1.
    Xiao, Q., Zhang, X.: A new AC servo motor load disturbance method. Adv. Sci. Technol. 2016, 313–317 (2006)Google Scholar
  2. 2.
    Iezawa, M., Imagi, A., Tomisawa, M.: High-precision control of AC servo motor positioning systems by friction compensation. JSME Int. J. 59(568), 3811–3816 (2008)Google Scholar
  3. 3.
    Kim, P.H., Sin, S.H., Baek, H.L., et al.: Speed control of AC servo motor using neural networks. In: International Conference on Electrical Machines and Systems, vol. 2, pp. 691–694. IEEE Xplore (2001)Google Scholar
  4. 4.
    Rogers, E., Owens, D.H., Werner, H., et al.: Norm-optimal iterative learning control with application to problems in accelerator-based free electron lasers and rehabilitation robotics. Eur. J. Control 16(5), 497–522 (2010)MathSciNetCrossRefzbMATHGoogle Scholar
  5. 5.
    Dong, Q.D., Vu, T.T., Han, H.C., et al.: Neuro-fuzzy control of interior permanent magnet synchronous motors. J. Electr. Eng. Technol. 8(6), 1439–1450 (2013)CrossRefGoogle Scholar
  6. 6.
    Cheng, S., Huang, Y.Y., Chou, H.H.: Dual robust controller design for high power AC servo drive. In: ECSIS Symposium on Learning and Adaptive Behaviors for Robotic Systems, pp. 97–102. IEEE Computer Society (2008)Google Scholar
  7. 7.
    Liang, J., Hu, Y., Lu, W.: Anti-disturbance adaptive control of permanent magnet AC servo system. Trans. China Electrotech. Soc. 26(10), 174–180 (2011)Google Scholar
  8. 8.
    Ito, K., Takahashi, H., Ikeo, S.: Comparative study of robust control for a water hydraulic servo motor system: 2nd report: disturbance observer and/or sliding mode control design approach. Trans. Jpn. Hydraul. Pneum. Soc. 38(2), 21–28 (2007)Google Scholar
  9. 9.
    Lu, W., Hu, Y., Liang, J., et al.: Anti-disturbance adaptive control for permanent magnet synchronous motor servo system. Proc. CSEE 31(3), 75–81 (2011)Google Scholar
  10. 10.
    Yoshitsugu, J., Inoue, K., Nakaoka, M.: Load speed observer-based fuzzy auto-tuning implementation for AC speed servo system with two-mass mechanical motion system-experimental verification. In: Industry Applications Conference, 1999. Thirty-Fourth IAS Annual Meeting. Conference Record of the 1999 IEEE, vol. 1, no. 2, pp. 645–652 (1999)Google Scholar
  11. 11.
    Yoshitsugu, J., Inoue, K., Nakaoka, M.: Load speed observer-based fuzzy auto-tuning implementation for AC speed servo system with two-mass mechanical motion system-experimental verification. In: Industry Applications Conference, 1999. Thirty-Fourth IAS Meeting. Conference Record of the IEEE, vol. 1, pp. 645–652 (2002)Google Scholar
  12. 12.
    Abdulhay, E., Mohammed, M.A., Ibrahim, D.A., Arunkumar, N., Venkatraman, V.: Computer aided solution for automatic segmenting and measurements of blood leucocytes using static microscope images. J. Med. Syst. (2018).  https://doi.org/10.1007/s10916-018-0912-y Google Scholar
  13. 13.
    Abdulhay, E., Arunkumar, N., Narasimhan, K., Vellaiappan, E., Venkatraman, V.: Gait and tremor investigation using machine learning techniques for the diagnosis of Parkinson disease. Fut. Gener. Comput. Syst. (2018).  https://doi.org/10.1016/j.future.2018.02.009 Google Scholar
  14. 14.
    Abdulhay, E., Elamaran, V., Arunkumar, N., Venkatraman, V.: Fault-tolerant medical imaging system with quintuple modular redundancy (QMR) configurations. J. Ambient Intell. Hum. Comput. (2018).  https://doi.org/10.1007/s12652-018-0748-9 Google Scholar
  15. 15.
    Vardhana, M., Arunkumar, N., Abdulhay, E., Ramirez-Gonzalez, G.: Convolutional neural network for bio-medical image segmentation with hardware acceleration. Cognit. Syst. Res. 50, 10–14 (2018)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.The State Key Lab of Fluid Power and Mechatronic Systems, College of Mechanical EngineeringZhejiang UniversityHangzhouChina
  2. 2.Key Laboratory of Advanced Manufacturing Technology of Zhejiang Province, College of Mechanical EngineeringZhejiang UniversityHangzhouChina

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