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Dergiler / Turkish Journal of Electrical Engineering and Computer Science / 2011 / Cilt: 19 - Sayı: 3

Online speed control of a brushless AC servomotor based on artificial neural networks

Sibel PARTAL
İbrahim ŞENOL
Ahmet Faruk BAKAN
Sayfa
373–383
Yayın Tarihi
2011-03-01
DOI
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⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

The complex servomotor hardware was highly simplified, the cost was significantly reduced, mechanical durability increased, maintenance need dropped considerably, low inertia was gained and the produced noise was noticeably lowered.

📌 3 Maddede Bu Makale

🎯
Amaç & Hipotez:In this paper, an alternative approach to speed estimation of brushless AC servomotors is presented.
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Temel Bulgular & Veri:Speed control is realized in the following steps.
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Sonuç & Katkı:The complex servomotor hardware was highly simplified, the cost was significantly reduced, mechanical durability increased, maintenance need dropped considerably, low inertia was gained and the produced noise was noticeably lowered.
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Key words: ANN · brushless servomotor · sensorless speed control

Özet

In this paper, an alternative approach to speed estimation of brushless AC servomotors is presented. Speed control is realized in the following steps. First, the servomotor was mathematically modelled; the driver system was designed and speed control of the servomotor was accomplished with feedback. Next, a network structure representing the electrical and mechanical properties of the servomotor was built via Artificial Neural Network (ANN) and trained with the results of the first step. The weights obtained from the neural nwork training were inserted into the control algorithm in accordance with the ANN. Finally, speed estimation was achieved based on the ANN. The complex servomotor hardware was highly simplified, the cost was significantly reduced, mechanical durability increased, maintenance need dropped considerably, low inertia was gained and the produced noise was noticeably lowered.

Abstract

In this paper, an alternative approach to speed estimation of brushless AC servomotors is presented. Speed control is realized in the following steps. First, the servomotor was mathematically modelled; the driver system was designed and speed control of the servomotor was accomplished with feedback. Next, a network structure representing the electrical and mechanical properties of the servomotor was built via Artificial Neural Network (ANN) and trained with the results of the first step. The weights obtained from the neural nwork training were inserted into the control algorithm in accordance with the ANN. Finally, speed estimation was achieved based on the ANN. The complex servomotor hardware was highly simplified, the cost was significantly reduced, mechanical durability increased, maintenance need dropped considerably, low inertia was gained and the produced noise was noticeably lowered.

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Bu Makaleye Atıf Yap

PARTAL, S., ŞENOL, İ., BAKAN, A. F. (2011). Online speed control of a brushless AC servomotor based on artificial neural networks. Turkish Journal of Electrical Engineering and Computer Science, Cilt: 19 - Sayı: 3, 373–383. https://ajindex.com/articles/189063

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Kaynakça (14)

  1. Y. Dote, S. Kinoshita, Brushless Servomotors Fundamentals and Applications, Clarendon Press, Oxford 1990.
  2. Y. Dote, Servomotor And Motion Control Using Digital Signal Processor, PrenticeHall Inc., New Jersey, 1990.
  3. D. J ouve, D. Bui, “Inşuence of the Motor Feedback Sensor on AC Brushless Servo Drive Performances”, PCIM’2003
  4. Conference, Nürnberg, May 20—22, 2003.
  5. L. Yongdong, Z. Hao, “Sensorless Control of Permanent Magnet Synchronous Motor 7 A Survey”, IEEE Vehicle Power and Propulsion Conference (VPPC), September 3-5, China, 2008.
  6. H.D. Taghirad, N. Abedi, E. Noohi, “A new sensorless vector control method for permanent magnet synchronous motor without velocity estimator”, AMC’02, 7th International Workshop on Advanced Motion Control, Maribor, Slovenia, 2002.
  7. A. Consoli, G. Scarcella, A. Testa, “Industry application of zero-speed sensorless control techniques for PM synchronous motors”, IEEE Trans. Ind. App. 37(2), pp. 513-519, 2001.
  8. S. Bolognani, M. Zigliotto, M. Zordan, “Extended-range PMSM sensorless speed drive based on stochastic filtering”, IEEE Trans. Power Elec., 16(1), 2001.
  9. M. Leksell, L. Harnefors, M. J ansson, “Direct sensorless speed control of PM—motors—a simple and effective sensorless method”, Published in the Proc. of Power Elec. Specialist Conference (PESC’01), 2001.
  10. D. Simon, D.L. Feucht, “Synchronous motor phase control by vector addition of induced winding voltages”, Industrial Electronics, IEEE Tran. 51(3), pp. 537-544, 2004.
  11. K.J. Hunt, D. Sbarbaro, R. Zbikowski, P.J. Gawthrop, “Neural Networks for Control Systems: A Survey, Automat ica”, pp. 1083-1122, 1992.
  12. S. Zorlu, I. Senol, A.F. Bakan, “Vector Control of an AC Brushless Servomotor Using a Custom-Designed Motion Control Card”, 2006 IEEE International Symposium on Industrial Electronics, 2516-2521,
  13. Montreal, July 2006.
  14. P. Vas, ArtiŞcal—Intelligence—Based Electrical Machines and Drives, Oxford University Pres, 1999. E. Meşe, “Sensorless Position Estimation for Switched Reluctance Motors Using ArtiŞcial Neural Networks”, Ph.D. E. Meşe, A.D. Torey, “An Approach for Sensorless Position Estimation for Switched Reluctance Motors Using Artificial Neural Networks”, IEEE Transactions On Power Electronics, Vol.17, No.1, 2002.