Journal of Advanced Marine Engineering and Technology

The Korean Society of Marine Engineering (한국마린엔지니어링학회)
권호 표지

Sensorless Speed Control of Induction Motor by Neural Network

신경회로망을 이용한 유도전동기의 센서리스 속도제어

  • Published : 2002.11.01
Download PDF
⟨ Previous Next ⟩

Abstract

Generally, induction motor controller requires rotor speed sensor for commutation and current control, but it increases cost and size of the motor. So in these days, various researches including speed sensorless vector control have been reported and some of them have been put to practical use. In this paper a new speed estimation method using neural networks is proposed. The optimal neural network structure was tracked down by trial and error, and it was found that the 8-16-1 neural network has given correct results for the instantaneous rotor speed. Supervised learning methods, through which the neural network is trained to learn the input/output pattern presented, are typically used. The back-propagation technique is used to adjust the neural network weights during training. The rotor speed is calculated by weights and eight inputs to the neural network. Also, the proposed method has advantages such as the independency on machine parameters, the insensitivity to the load condition, and the stability in the low speed operation.

Keywords

References

  1. Sensorless Control of AC Motor Drives Kaushik Rajashekara;Atsuo Kawamura;Kouki Matsue
  2. IEEE IECON Com- parison of Different Schemes without Shaft Sensors for Field Oriented Control Drives A. Bettini;L. Ferraris;G. Griva;F. Profumo
  3. IEEE PCC Improvement of a Stator Flux- Oriented Speed Sensorless Control of an Induction Motor A. Gastli;M. Tomita;T. Takeshita;N. Matsui
  4. Proc. Intl. Power Electron. Conf. Speed Sensorless Vector Control Method for an Industrial Drive System H. Tajima;Y. Matsumoto;H. Umida;M. Kawano
  5. IEEE IAS Ann. Mtg. Speed Sensorless Hybrid Vector Controlled Induction Motor Drive B. K. Bose;M. G. Simoes
  6. IEEE Trans. on Neural Networks v.1 no.1 Identification and Control of Dynamical Systems Using Neural Networks K. S. Narendra;K. Parthasarathy https://doi.org/10.1109/72.80202
  7. IEEE Trans. on Auto. Con. v.1 no.5 Adaptive Control of a Class of Nonlinear Discrete-Time Systems Using Neural Networks F. C. Chen;H. K. Khalil
  8. Intelligent Control Systems M. M. Gupta;N. K. Sinha
  9. IEEE Trans. on Ind. Appl. v.31 no.3 Neural Network Based Estimation of Feedback Signals for a Vector Controlled Induction Motor Drive M. G. Simoes;B. K. Bose https://doi.org/10.1109/28.382124
  10. IEEE Trans. on Ind. Appl. v.31 no.3 Identification and Control of Induction Machines Using Artificial Neural Networks M. T. Wishart;R. G. Harley https://doi.org/10.1109/28.382123
  11. IEEE PCC-yohohama Identification of In duction Motor Speed using Neural Networks L. B. Brahim;R. Kurosawa
  12. 퍼지·신경망·유전진화 임영도
  13. Neuro-Fuzzy and Soft Computing J. -S. R. Jang;C.-T. Sun;E. Mizutani
  14. The Field Orientation Principle in Control of Induction Motors Andrzej M. Trzynadlowski
  15. Analysis of Electrical Machinery P. C. Krause
  16. Analysis of Electrical Machinery P. C. Krause