BibTex RIS Kaynak Göster

Speed Control of Vector Controlled Induction Motors under Variable Load with RBFNN Based Model Reference Adaptive Control

Yıl 2015, Cilt: 3 Sayı: 3, 27 - 33, 14.11.2015
https://doi.org/10.5505/apjes.2015.20591

Öz

In the speed control of induction motors, an acceptable good performance cannot be obtained by using traditional feedback controllers due to the non-linear structure of the system, the effects of changing environmental conditions and several disturbance inputs. On the other hand, in recent years, it has been demonstrated that artificial intelligence based control methods were much more successful in the nonlinear system control applications. In this study, an intelligent controller has been developed for speed control of induction motors by using radial basis function neural network (RBFNN) and model reference adaptive control (MRAC) strategy. In the driving of induction motor, indirect field oriented vector control method which is widely used in high-performance drive system has been preferred. Simulation results to determine the success of the development of this control method was compared with conventional PI type controller. While the motor is under the fan-type load, the performance of controller has been investigated in Matlab/Simulink environment. The simulation results demonstrate that the performance of RBFNN based MRAC controller is better than that of conventional PI controller.

Kaynakça

  • Chan, T.F. ve Shi, K., “Applied Intelligent Control of Induction Motor Drives”, IEEE Willey Press, First edition, 2011.
  • Sarıoğlu, M.K., Gökaşan, M. ve Boğosyan, S., “Asenkron
  • Yayınevi, İstanbul, 2003.
  • Menghal, P.M. ve Laxmi, A.J., “Artificial Intelligence Based Dynamic Simulation of Induction Motor Drives”, IOSR Journal of Electrical and Electronics Engineering (IOSR-JEEE), ISSN: 2278- 1676 Volume 3, Issue 5, PP 37-45, 2012.
  • Bose, B.K., “Adjustable Speed AC Drive System”, IEEE press, New York, 1981.
  • Ozcalik, H.R., Yıldız, C., Koca, Z., Doganay, S., “An Adaptive Neural Controller for Induction Motor Based on Volt-Hertz Driving Method”, Inista’07, p:96-100, Istanbul, Turkey, 2007.
  • Ozcalik, H.R., Yıldız, C., Danaci, M., Koca, Z., “RBF Based Induction Motor Control with a Good Nonlinearity Compensation”, Lecture Notes in Computer Science, 4507: 878-886, 2007.
  • Demirbaş, Ş., Irmak, E., Bayhan, S. ve Çolak, İ., “Mikrodenetleyici ile Rotoru Sargılı Asenkron Motor Rotor Direncinin Değiştirilerek Tork ve Hız Kontrolü”, Gazi Üniv. Müh. Mim. Fak. Der. Cilt 23, No 4, 801-809, 2008.
  • Açıkgöz, H., Şekkeli, M., “Bulanık Mantık Denetleyici ile Doğrudan Moment Denetim Yöntemi Uygulanan Asenkron Motorun Hız Denetim Performansının İncelenmesi”, Akademik Platform Mühendislik ve Fen Bilimleri Dergisi (APJES) Cilt.1, Sayı.2, S.50-57, 2013.
  • Vas, P., “Sensorless Vector and Direct Torque Control”, New York: Oxford University Press, 1998. [10]. Tripura, P., Babu, Y.S.K., “Fuzzy Logic Speed Control of Three Phase Induction Motor Drive” World Academy of Science, Engineering and Technology 60, 2011.
  • Mahesh, A., Singh, B., “Vector Control of Induction Motor Using ANN and Particle Swarm Optimization”, International Journal of Emerging Technology and Advanced Engineering, ISSN 2250- 2459, Volume 2, Issue 9, 2012.
  • Santisteban, J.A., Stephan, R.M., “Vector Control Methods for Induction Machines:An Overview”, IEEE Transactions on Education, Vol.44, No.2, 2001.
  • Jain, P., Nigam, M.J., “Design of a Model Reference Adaptive Controller Using Modified MIT Rule for a Second Order System”, Advance in Electronic and Electric Engineering, ISSN 2231- 1297, Volume 3, Number 4, pp. 477-484, 2013.
  • Loire, S., Fonoberov, V.A. ve Mezic, I., “Performance Study of an Adaptive Controller in the Precense
  • Technology, IEEE Transactions on Vol:21, Issue:3, 2013.
  • Zilkova, J., Timko, J., Girovsky, P., “Nonlinear System Control Using Neural Networks”, Acta Ploytechnica Hungarica, Vol.3, No.4, 2006.
  • Brandstetter, P., Bilek, P., “Applications of Artificial Neural Networks in Control of DC Drive”, International
  • ICEUTE’12 - SOCO’12 Special Sessions Advanced in Intelligent Systems and Computing Volume 189, pp.351-360, 2013.
  • Zhang, M., Li, W. ve Liu, M., “Adaptive PID Control Strategy Based on RBF Neural Network Identification”, Neural Networks and Brain, ICNN&B'05,
  • on (Volume:3 ), published by IEEE, pp. 1854-1857, 2005.
  • Zerikat, M., Chekroun, S., Mechernene, A., “Fuzzy-Neural
  • Adaptation Mechanism for MRAS Sensorless Induction Motor Drives”, ELECTROMOTION 2009, EPE Chapter “Electric Drives” Joint Symposium, Lille, France, 2009. Systems Joint Conference CISIS’12
  • - Conference Controller-Based [19]. Amrane, A., Louri, M., Larabi, A., Hamzaoui, A., “A Fuzzy Model Reference Adaptive System Control for Induction Motor Drives”, Proceedings of the 3rd International Conference on Systems and Control, Algiers, Algeria, October 29-31, 2013.
  • Zhou, Y., Li, Y., Zheng, Z., “Research of Speed Sensorless Vector Control of an Induction Motor Based on Model Reference Adaptive System”, International Conference on Electrical Machines and Systems, ICEMS 2008, pp:1381-1384, 2008.
  • Rezgui, S.E. ve Benalla, H., “High Performance Controllers for Speed and Position Induction Motor Drive using New Reaching Law”, International Journal of Instrumentation and Control Systems (IJICS) Vol.1, No.2, 2011.
  • Zareen J. Tamboli, Z.J., Khot, S.R., “Estimated Analysis of Radial Basis Function Neural Network for Induction Motor Fault Detection”, International Journal of Engineering and Advanced Technology (IJEAT), ISSN: 2249 – 8958, Volume-2, Issue-4, 2013. [23]. Haykin,
  • Comprehensive Foundation”, 2nd Edition Prentice Hall, Upper Saddle River, New Jersey, USA, 1999. [24]. Canigür, E., “Gezgin Robotlar için Model Referans Uyarlamalı Yörünge Takip Kontrolü”, Yüksek Networks:
  • Üniversitesi, Fen Bilimleri Enstitüsü, 2012. A Lisans Eskişehir
  • Osmangazi 18, 2015). Yıldız, C., İmal, M., for industrial
  • applications”, System”, International Journal of Mathworks,URL:

Vektör Kontrollü Asenkron Motorların RTYSA Temelli Model Referans Adaptif Kontrol ile Değişken Yük Altında Hız Denetimi

Yıl 2015, Cilt: 3 Sayı: 3, 27 - 33, 14.11.2015
https://doi.org/10.5505/apjes.2015.20591

Öz

Asenkron motorların hız denetiminde, sistemin doğrusal olmayan yapısı, değişen çevre koşulları ve bozucu girişlerin etkisi nedeniyle geleneksel geri beslemeli denetleyiciler ile iyi bir performans elde edilememektedir. Asenkron motor sürücülerinin performansının arttırılmasında yapay zekâ tabanlı yöntemlerin kullanılmasının yararları son yıllardaki araştırmalarla açık bir şekilde ortaya konulmuştur. Bu çalışmada radyal tabanlı yapay sinir ağlarından (RTYSA) ve model referans adaptif kontrol (MRAK) yapısından faydalanılarak asenkron motorların hız denetimi için yapay zekâ esaslı bir denetleyici geliştirilmiştir. Asenkron motorun sürme yönteminde, yüksek performanslı sürücü sistemlerinde yaygın olarak kullanılan dolaylı alan yönlendirmeli vektör kontrol tekniği tercih edilmiştir. Geliştirilen bu denetim yönteminin başarısını belirlemek amacıyla benzetim sonuçları geleneksel PI-tipi denetleyici ile karşılaştırılmıştır. Motor fan tipi yük altında iken, denetleyicinin performansı Matlab/Simulink ortamında incelenmiştir. Simülasyon sonuçları RTYSA temelli MRAK denetleyici performansının, geleneksel PI denetleyiciden daha iyi olduğunu göstermiştir.

Kaynakça

  • Chan, T.F. ve Shi, K., “Applied Intelligent Control of Induction Motor Drives”, IEEE Willey Press, First edition, 2011.
  • Sarıoğlu, M.K., Gökaşan, M. ve Boğosyan, S., “Asenkron
  • Yayınevi, İstanbul, 2003.
  • Menghal, P.M. ve Laxmi, A.J., “Artificial Intelligence Based Dynamic Simulation of Induction Motor Drives”, IOSR Journal of Electrical and Electronics Engineering (IOSR-JEEE), ISSN: 2278- 1676 Volume 3, Issue 5, PP 37-45, 2012.
  • Bose, B.K., “Adjustable Speed AC Drive System”, IEEE press, New York, 1981.
  • Ozcalik, H.R., Yıldız, C., Koca, Z., Doganay, S., “An Adaptive Neural Controller for Induction Motor Based on Volt-Hertz Driving Method”, Inista’07, p:96-100, Istanbul, Turkey, 2007.
  • Ozcalik, H.R., Yıldız, C., Danaci, M., Koca, Z., “RBF Based Induction Motor Control with a Good Nonlinearity Compensation”, Lecture Notes in Computer Science, 4507: 878-886, 2007.
  • Demirbaş, Ş., Irmak, E., Bayhan, S. ve Çolak, İ., “Mikrodenetleyici ile Rotoru Sargılı Asenkron Motor Rotor Direncinin Değiştirilerek Tork ve Hız Kontrolü”, Gazi Üniv. Müh. Mim. Fak. Der. Cilt 23, No 4, 801-809, 2008.
  • Açıkgöz, H., Şekkeli, M., “Bulanık Mantık Denetleyici ile Doğrudan Moment Denetim Yöntemi Uygulanan Asenkron Motorun Hız Denetim Performansının İncelenmesi”, Akademik Platform Mühendislik ve Fen Bilimleri Dergisi (APJES) Cilt.1, Sayı.2, S.50-57, 2013.
  • Vas, P., “Sensorless Vector and Direct Torque Control”, New York: Oxford University Press, 1998. [10]. Tripura, P., Babu, Y.S.K., “Fuzzy Logic Speed Control of Three Phase Induction Motor Drive” World Academy of Science, Engineering and Technology 60, 2011.
  • Mahesh, A., Singh, B., “Vector Control of Induction Motor Using ANN and Particle Swarm Optimization”, International Journal of Emerging Technology and Advanced Engineering, ISSN 2250- 2459, Volume 2, Issue 9, 2012.
  • Santisteban, J.A., Stephan, R.M., “Vector Control Methods for Induction Machines:An Overview”, IEEE Transactions on Education, Vol.44, No.2, 2001.
  • Jain, P., Nigam, M.J., “Design of a Model Reference Adaptive Controller Using Modified MIT Rule for a Second Order System”, Advance in Electronic and Electric Engineering, ISSN 2231- 1297, Volume 3, Number 4, pp. 477-484, 2013.
  • Loire, S., Fonoberov, V.A. ve Mezic, I., “Performance Study of an Adaptive Controller in the Precense
  • Technology, IEEE Transactions on Vol:21, Issue:3, 2013.
  • Zilkova, J., Timko, J., Girovsky, P., “Nonlinear System Control Using Neural Networks”, Acta Ploytechnica Hungarica, Vol.3, No.4, 2006.
  • Brandstetter, P., Bilek, P., “Applications of Artificial Neural Networks in Control of DC Drive”, International
  • ICEUTE’12 - SOCO’12 Special Sessions Advanced in Intelligent Systems and Computing Volume 189, pp.351-360, 2013.
  • Zhang, M., Li, W. ve Liu, M., “Adaptive PID Control Strategy Based on RBF Neural Network Identification”, Neural Networks and Brain, ICNN&B'05,
  • on (Volume:3 ), published by IEEE, pp. 1854-1857, 2005.
  • Zerikat, M., Chekroun, S., Mechernene, A., “Fuzzy-Neural
  • Adaptation Mechanism for MRAS Sensorless Induction Motor Drives”, ELECTROMOTION 2009, EPE Chapter “Electric Drives” Joint Symposium, Lille, France, 2009. Systems Joint Conference CISIS’12
  • - Conference Controller-Based [19]. Amrane, A., Louri, M., Larabi, A., Hamzaoui, A., “A Fuzzy Model Reference Adaptive System Control for Induction Motor Drives”, Proceedings of the 3rd International Conference on Systems and Control, Algiers, Algeria, October 29-31, 2013.
  • Zhou, Y., Li, Y., Zheng, Z., “Research of Speed Sensorless Vector Control of an Induction Motor Based on Model Reference Adaptive System”, International Conference on Electrical Machines and Systems, ICEMS 2008, pp:1381-1384, 2008.
  • Rezgui, S.E. ve Benalla, H., “High Performance Controllers for Speed and Position Induction Motor Drive using New Reaching Law”, International Journal of Instrumentation and Control Systems (IJICS) Vol.1, No.2, 2011.
  • Zareen J. Tamboli, Z.J., Khot, S.R., “Estimated Analysis of Radial Basis Function Neural Network for Induction Motor Fault Detection”, International Journal of Engineering and Advanced Technology (IJEAT), ISSN: 2249 – 8958, Volume-2, Issue-4, 2013. [23]. Haykin,
  • Comprehensive Foundation”, 2nd Edition Prentice Hall, Upper Saddle River, New Jersey, USA, 1999. [24]. Canigür, E., “Gezgin Robotlar için Model Referans Uyarlamalı Yörünge Takip Kontrolü”, Yüksek Networks:
  • Üniversitesi, Fen Bilimleri Enstitüsü, 2012. A Lisans Eskişehir
  • Osmangazi 18, 2015). Yıldız, C., İmal, M., for industrial
  • applications”, System”, International Journal of Mathworks,URL:
Toplam 30 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Bölüm Makaleler
Yazarlar

Erdal Kılıç Bu kişi benim

Hasan Rıza Özçalık Bu kişi benim

Şaban Yılmaz Bu kişi benim

Sami Şit

Yayımlanma Tarihi 14 Kasım 2015
Gönderilme Tarihi 14 Kasım 2015
Yayımlandığı Sayı Yıl 2015 Cilt: 3 Sayı: 3

Kaynak Göster

IEEE E. . Kılıç, H. R. . Özçalık, Ş. . Yılmaz, ve S. . Şit, “Vektör Kontrollü Asenkron Motorların RTYSA Temelli Model Referans Adaptif Kontrol ile Değişken Yük Altında Hız Denetimi”, APJES, c. 3, sy. 3, ss. 27–33, 2015, doi: 10.5505/apjes.2015.20591.