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Speed-sensorless predictive torque control of the IM based on MRAS

Yıl 2023, Cilt 12, Sayı 1, 126 - 133, 15.01.2023
https://doi.org/10.28948/ngumuh.1208031

Öz

In this study, an induction motor (IM) drive based on speed-sensorless predictive torque control (PTC) is designed to perform the high-performance control of the IMs by utilizing the least mean square (LMS) algorithm for the adaptation mechanism of the model reference adaptive system (MRAS). Here, the MRAS with LMS adaptation is based on the stator currents (i_sα and i_sβ) of the IM. Moreover, the rotor fluxes (φ_rα and φ_rβ) are obtained by the current model, which requires the rotor mechanical speed (ω_m) along with i_sα and i_sβ. In contrast to the other MRAS based studies using proportional-integral (PI) in the adaptation mechanisms to estimate state or parameter, it is possible to determine the states and/or parameters as weight coefficients in the MRAS with LMS adaptation which are calculated and updated in each iteration. Here, ω_m value is estimated and updated in each iteration as weight coefficient. Furthermore, the MRAS with LMS adaptation is compared to the MRAS using conventional PI in simulations. The simulation results clearly visualize both the estimation performance of stator current based MRAS using LMS adaptation and the effectiveness of the proposed PTC based IM drive.

Kaynakça

  • B. Reddy, G. Poddar, and B. P. Muni, ‘Parameter Estimation and Online Adaptation of Rotor Time Constant for Induction Motor Drive’, IEEE Trans. Ind. Appl., 58(2), 1416–1428, 2022. https://doi.org/10.1109 /TIA.2022.3141700.
  • I. M. Alsofyani and N. R. N. Idris, ‘Simple Flux Regulation for Improving State Estimation at Very Low and Zero Speed of a Speed Sensorless Direct Torque Control of an Induction Motor’, IEEE Trans. Power Electron., 31(4), 3027–3035, 2016. https://doi.org/10.1109/TPEL.2015.2447731.
  • M. A. Usta, H. I. Okumus, and H. Kahveci, ‘A simplified three-level SVM-DTC induction motor drive with speed and stator resistance estimation based on extended Kalman filter’, Electr. Eng., 99(2), 707–720, 2017. https://doi.org/10.1007/s00202-016-0442-x.
  • J. Rodriguez, R. M. Kennel, J. R. Espinoza, M. Trincado, C. A. Silva, and C. A. Rojas, ‘High-Performance Control Strategies for Electrical Drives: An Experimental Assessment’, IEEE Trans. Ind. Electron., 59(2), 812–820, 2012. https://doi.org/10.11 09/TIE.2011.2158778.
  • K. Wróbel, P. Serkies, and K. Szabat, ‘Model Predictive Base Direct Speed Control of Induction Motor Drive—Continuous and Finite Set Approaches’, Energies, 13(5), 1193, 2020. https://doi.org/10.3390 /en13051193.
  • D. Casadei, F. Profumo, G. Serra, and A. Tani, ‘FOC and DTC: two viable schemes for induction motors torque control’, IEEE Trans. Power Electron., 17(5), 779–787, 2002. https://doi.org/10.1109/TPEL.2002.80 2183.
  • F. Korkmaz, I. Topaloglu, H. Mamur, M. Ari, and I. Tarimer, ‘Reduction of torque ripple in induction motor by artificial neural multinetworks’, Turk. J. Electr. Eng. Comput. Sci., 24, 3492–3502, 2016. https://doi.org /10.3906/elk-1406-54.
  • E. Zerdali̇ and R. Demi̇r, ‘Speed-sensorless predictive torque controlled induction motor drive with feed-forward control of load torque for electric vehicle applications’, Turk. J. Electr. Eng. Comput. Sci., 29, 223–240, 2021. https://doi.org/10.3906/elk-2005-75.
  • K. V Praveen Kumar and T. V. Kumar, ‘Enhanced direct torque control and predictive torque control strategies of an open‐End winding induction motor drive to eliminate common‐mode voltage and weighting factors’, IET Power Electron., 12(8), 1986–1997, 2019. https://doi.org/10.1049/iet-pel.2018.5599.
  • S. R. Eftekhari, S. A. Davari, P. Naderi, C. Garcia, and J. Rodriguez, ‘Robust Loss Minimization for Predictive Direct Torque and Flux Control of an Induction Motor With Electrical Circuit Model’, IEEE Trans. Power Electron., 35(5), 5417–5426, 2020. https://doi.org/10.1 109/TPEL.2019.2944190.
  • P. R. U. Guazzelli, W. C. de Andrade Pereira, C. M. R. de Oliveira, A. G. de Castro, and M. L. de Aguiar, ‘Weighting Factors Optimization of Predictive Torque Control of Induction Motor by Multiobjective Genetic Algorithm’, IEEE Trans. Power Electron., 34(7), 6628–6638, 2019. https://doi.org/10.1109/TPEL.2018. 2834304.
  • F. Wang, H. Xie, Q. Chen, S. A. Davari, J. Rodriguez, and R. Kennel, ‘Parallel Predictive Torque Control for Induction Machines Without Weighting Factors’, IEEE Trans. Power Electron., 35(2), 1779–1788, 2020. https ://doi.org/10.1109/TPEL.2019.2922312.
  • J. Wang, F. Wang, Z. Zhang, S. Li, and J. Rodriguez, ‘Design and Implementation of Disturbance Compensation-Based Enhanced Robust Finite Control Set Predictive Torque Control for Induction Motor Systems’, IEEE Trans. Ind. Inform., 13(5), 2645–2656, 2017. https://doi.org/10.1109/TII.2017.2679283.
  • S. A. Bednarz and M. Dybkowski, ‘Estimation of the Induction Motor Stator and Rotor Resistance Using Active and Reactive Power Based Model Reference Adaptive System Estimator’, Appl. Sci., 9(23), 5145, 2019. https://doi.org/10.3390/app9235145.
  • I. Vicente, A. Endeman, X. Garin, and M. Brown, ‘Comparative study of stabilising methods for adaptive speed sensorless full-order observers with stator resistance estimation’, IET Control Theory Appl., 4(6), 993–1004, 2010. https://doi.org/10.1049/iet-cta.2008 .0506.
  • Y. Zhang, Z. Yin, Y. Zhang, J. Liu, and X. Tong, ‘A Novel Sliding Mode Observer With Optimized Constant Rate Reaching Law for Sensorless Control of Induction Motor’, IEEE Trans. Ind. Electron., 67(7), 5867–5878, 2020. https://doi.org/10.1109/TIE.2019.29 42577.
  • R. Yildiz, M. Barut, and E. Zerdali, ‘A Comprehensive Comparison of Extended and Unscented Kalman Filters for Speed-Sensorless Control Applications of Induction Motors’, IEEE Trans. Ind. Inform., 16(10), 6423–6432, 2020. https://doi.org/10.1109/TII.2020.29 64876.
  • C. Schauder, ‘Adaptive speed identification for vector control of induction motors without rotational transducers’, IEEE Trans. Ind. Appl., 28(5), 1054–1061, 1992. https://doi.org/10.1109/28.158829.
  • V. Vasic, S. N. Vukosavic, and E. Levi, ‘A stator resistance estimation scheme for speed sensorless rotor flux oriented induction motor drives’, IEEE Trans. Energy Convers., 18(4), 476–483, 2003. https://doi .org10.1109/TEC.2003.816595.
  • M. N. Gayathri, S. Himavathi, and R. Sankaran, Performance enhancement of vector controlled drive with rotor flux based MRAS rotor resistance estimator. International Conference on Computer Communication and Informatics (ICCCI -2012), pp. 1–6, Coimbatore, India, 2012.
  • F. L. Mapelli, D. Tarsitano, and F. Cheli, ‘MRAS rotor resistance estimators for EV vector controlled induction motor traction drive: Analysis and experimental results’, Electr. Power Syst. Res., 146, 298–307, 2017. https://doi.org/10.1016/j.epsr.2017.02. 005.
  • R. Demir and M. Barut, ‘Novel hybrid estimator based on model reference adaptive system and extended Kalman filter for speed-sensorless induction motor control’, Trans. Inst. Meas. Control, 40(13), 3884–3898, 2018. https://doi.org/10.1177/01423312177346 31.
  • A. V. R. Teja, C. Chakraborty, S. Maiti, and Y. Hori, ‘A New Model Reference Adaptive Controller for Four Quadrant Vector Controlled Induction Motor Drives’, IEEE Trans. Ind. Electron., 59(10), 3757–3767, 2012. https://doi.org/10.1109/TIE.2011.2164769.
  • S. Basak, A. V. Ravi Teja, C. Chakraborty, and Y. Hori, A new model reference adaptive formulation to estimate stator resistance in field oriented induction motor drive. 39th Annual Conference of the IEEE Industrial Electronics Society (IECON 2013), pp. 8470–8475, Vienna, Austria, 2013.
  • T. Orlowska-Kowalska and M. Dybkowski, ‘Stator-Current-Based MRAS Estimator for a Wide Range Speed-Sensorless Induction-Motor Drive’, IEEE Trans. Ind. Electron., 57(4), 1296–1308, 2010. https:// doi.org/10.1109/TIE.2009.2031134.
  • M. Barut, S. Bogosyan, and M. Gokasan, ‘Speed-Sensorless Estimation for Induction Motors Using Extended Kalman Filters’, IEEE Trans. Ind. Electron., 54(1), 272–280, 2007. https://doi.org/10.1109/TIE.200 6.885123.
  • S. Haykin, Adaptive filter theory (3rd ed.). Prentice-Hall, Inc., Upper Saddle River, NJ, USA, 1996.
  • R. Yildiz, R. Demir, and M. Barut, Speed-sensorless predictive torque control of IM based on the adaptive fading extended Kalman filter. IV. International Turkic World Congress on Science and Engineering, pp. 82–93. Niğde, Turkey, 2022.
  • M. Habibullah, D. D. C. Lu, D. Xiao, J. E. Fletcher, and M. F. Rahman, ‘Predictive Torque Control of Induction Motor Sensorless Drive Fed by a 3L-NPC Inverter’, IEEE Trans. Ind. Inform., 13(1), 60–70, 2017. https:// doi.org/10.1109/TII.2016.2603922.

ASM’nin MUS tabanlı hız-algılayıcısız öngörülü moment kontrolü

Yıl 2023, Cilt 12, Sayı 1, 126 - 133, 15.01.2023
https://doi.org/10.28948/ngumuh.1208031

Öz

Bu çalışmada, asenkron motorların (ASM’lerin) yüksek başarımlı kontrolünü gerçekleştirmek için uyarlama mekanizmasında en küçük ortalama kareler (EKOK) algoritmasını kullanan modele uyarlamalı sisteme (MUS’a) dayanan hız-algılayıcısız öngörülü moment kontrol (ÖMK) tabanlı ASM sürücüsü tasarlanmıştır. Burada, EKOK uyarlamalı MUS ASM’nin stator akımları (i_sα ve i_sβ) tabanlıdır. Rotor akıları (φ_rα ve φ_rβ), rotor mekanik hızı (ω_m) ile birlikte i_sα ve i_sβ gerektiren akım model kullanılarak elde edilmiştir. Uyarlama mekanizmasında oransal-integral kullanan MUS tabanlı çalışmaların aksine, EKOK uyarlamalı MUS’da durum ve/veya parametreler her iterasyonda hesaplanan ve güncellenen ağırlık katsayıları olarak tanımlanabilir. Bu çalışmada ω_m her iterasyonda ağırlık katsayısı olarak kestirilir ve güncellenir. Ayrıca, EKOK uyarlamalı MUS geleneksel oransal-integrali kullanan MUS ile benzetim ortamında karşılaştırılmıştır. Benzetim sonuçları EKOK uyarlamasını kullanan stator akımları tabanlı MUS’un kestirim başarımını ve önerilen ÖMK tabanlı ASM sürücüsünün etkinliğini açıkça göstermektedir.

Kaynakça

  • B. Reddy, G. Poddar, and B. P. Muni, ‘Parameter Estimation and Online Adaptation of Rotor Time Constant for Induction Motor Drive’, IEEE Trans. Ind. Appl., 58(2), 1416–1428, 2022. https://doi.org/10.1109 /TIA.2022.3141700.
  • I. M. Alsofyani and N. R. N. Idris, ‘Simple Flux Regulation for Improving State Estimation at Very Low and Zero Speed of a Speed Sensorless Direct Torque Control of an Induction Motor’, IEEE Trans. Power Electron., 31(4), 3027–3035, 2016. https://doi.org/10.1109/TPEL.2015.2447731.
  • M. A. Usta, H. I. Okumus, and H. Kahveci, ‘A simplified three-level SVM-DTC induction motor drive with speed and stator resistance estimation based on extended Kalman filter’, Electr. Eng., 99(2), 707–720, 2017. https://doi.org/10.1007/s00202-016-0442-x.
  • J. Rodriguez, R. M. Kennel, J. R. Espinoza, M. Trincado, C. A. Silva, and C. A. Rojas, ‘High-Performance Control Strategies for Electrical Drives: An Experimental Assessment’, IEEE Trans. Ind. Electron., 59(2), 812–820, 2012. https://doi.org/10.11 09/TIE.2011.2158778.
  • K. Wróbel, P. Serkies, and K. Szabat, ‘Model Predictive Base Direct Speed Control of Induction Motor Drive—Continuous and Finite Set Approaches’, Energies, 13(5), 1193, 2020. https://doi.org/10.3390 /en13051193.
  • D. Casadei, F. Profumo, G. Serra, and A. Tani, ‘FOC and DTC: two viable schemes for induction motors torque control’, IEEE Trans. Power Electron., 17(5), 779–787, 2002. https://doi.org/10.1109/TPEL.2002.80 2183.
  • F. Korkmaz, I. Topaloglu, H. Mamur, M. Ari, and I. Tarimer, ‘Reduction of torque ripple in induction motor by artificial neural multinetworks’, Turk. J. Electr. Eng. Comput. Sci., 24, 3492–3502, 2016. https://doi.org /10.3906/elk-1406-54.
  • E. Zerdali̇ and R. Demi̇r, ‘Speed-sensorless predictive torque controlled induction motor drive with feed-forward control of load torque for electric vehicle applications’, Turk. J. Electr. Eng. Comput. Sci., 29, 223–240, 2021. https://doi.org/10.3906/elk-2005-75.
  • K. V Praveen Kumar and T. V. Kumar, ‘Enhanced direct torque control and predictive torque control strategies of an open‐End winding induction motor drive to eliminate common‐mode voltage and weighting factors’, IET Power Electron., 12(8), 1986–1997, 2019. https://doi.org/10.1049/iet-pel.2018.5599.
  • S. R. Eftekhari, S. A. Davari, P. Naderi, C. Garcia, and J. Rodriguez, ‘Robust Loss Minimization for Predictive Direct Torque and Flux Control of an Induction Motor With Electrical Circuit Model’, IEEE Trans. Power Electron., 35(5), 5417–5426, 2020. https://doi.org/10.1 109/TPEL.2019.2944190.
  • P. R. U. Guazzelli, W. C. de Andrade Pereira, C. M. R. de Oliveira, A. G. de Castro, and M. L. de Aguiar, ‘Weighting Factors Optimization of Predictive Torque Control of Induction Motor by Multiobjective Genetic Algorithm’, IEEE Trans. Power Electron., 34(7), 6628–6638, 2019. https://doi.org/10.1109/TPEL.2018. 2834304.
  • F. Wang, H. Xie, Q. Chen, S. A. Davari, J. Rodriguez, and R. Kennel, ‘Parallel Predictive Torque Control for Induction Machines Without Weighting Factors’, IEEE Trans. Power Electron., 35(2), 1779–1788, 2020. https ://doi.org/10.1109/TPEL.2019.2922312.
  • J. Wang, F. Wang, Z. Zhang, S. Li, and J. Rodriguez, ‘Design and Implementation of Disturbance Compensation-Based Enhanced Robust Finite Control Set Predictive Torque Control for Induction Motor Systems’, IEEE Trans. Ind. Inform., 13(5), 2645–2656, 2017. https://doi.org/10.1109/TII.2017.2679283.
  • S. A. Bednarz and M. Dybkowski, ‘Estimation of the Induction Motor Stator and Rotor Resistance Using Active and Reactive Power Based Model Reference Adaptive System Estimator’, Appl. Sci., 9(23), 5145, 2019. https://doi.org/10.3390/app9235145.
  • I. Vicente, A. Endeman, X. Garin, and M. Brown, ‘Comparative study of stabilising methods for adaptive speed sensorless full-order observers with stator resistance estimation’, IET Control Theory Appl., 4(6), 993–1004, 2010. https://doi.org/10.1049/iet-cta.2008 .0506.
  • Y. Zhang, Z. Yin, Y. Zhang, J. Liu, and X. Tong, ‘A Novel Sliding Mode Observer With Optimized Constant Rate Reaching Law for Sensorless Control of Induction Motor’, IEEE Trans. Ind. Electron., 67(7), 5867–5878, 2020. https://doi.org/10.1109/TIE.2019.29 42577.
  • R. Yildiz, M. Barut, and E. Zerdali, ‘A Comprehensive Comparison of Extended and Unscented Kalman Filters for Speed-Sensorless Control Applications of Induction Motors’, IEEE Trans. Ind. Inform., 16(10), 6423–6432, 2020. https://doi.org/10.1109/TII.2020.29 64876.
  • C. Schauder, ‘Adaptive speed identification for vector control of induction motors without rotational transducers’, IEEE Trans. Ind. Appl., 28(5), 1054–1061, 1992. https://doi.org/10.1109/28.158829.
  • V. Vasic, S. N. Vukosavic, and E. Levi, ‘A stator resistance estimation scheme for speed sensorless rotor flux oriented induction motor drives’, IEEE Trans. Energy Convers., 18(4), 476–483, 2003. https://doi .org10.1109/TEC.2003.816595.
  • M. N. Gayathri, S. Himavathi, and R. Sankaran, Performance enhancement of vector controlled drive with rotor flux based MRAS rotor resistance estimator. International Conference on Computer Communication and Informatics (ICCCI -2012), pp. 1–6, Coimbatore, India, 2012.
  • F. L. Mapelli, D. Tarsitano, and F. Cheli, ‘MRAS rotor resistance estimators for EV vector controlled induction motor traction drive: Analysis and experimental results’, Electr. Power Syst. Res., 146, 298–307, 2017. https://doi.org/10.1016/j.epsr.2017.02. 005.
  • R. Demir and M. Barut, ‘Novel hybrid estimator based on model reference adaptive system and extended Kalman filter for speed-sensorless induction motor control’, Trans. Inst. Meas. Control, 40(13), 3884–3898, 2018. https://doi.org/10.1177/01423312177346 31.
  • A. V. R. Teja, C. Chakraborty, S. Maiti, and Y. Hori, ‘A New Model Reference Adaptive Controller for Four Quadrant Vector Controlled Induction Motor Drives’, IEEE Trans. Ind. Electron., 59(10), 3757–3767, 2012. https://doi.org/10.1109/TIE.2011.2164769.
  • S. Basak, A. V. Ravi Teja, C. Chakraborty, and Y. Hori, A new model reference adaptive formulation to estimate stator resistance in field oriented induction motor drive. 39th Annual Conference of the IEEE Industrial Electronics Society (IECON 2013), pp. 8470–8475, Vienna, Austria, 2013.
  • T. Orlowska-Kowalska and M. Dybkowski, ‘Stator-Current-Based MRAS Estimator for a Wide Range Speed-Sensorless Induction-Motor Drive’, IEEE Trans. Ind. Electron., 57(4), 1296–1308, 2010. https:// doi.org/10.1109/TIE.2009.2031134.
  • M. Barut, S. Bogosyan, and M. Gokasan, ‘Speed-Sensorless Estimation for Induction Motors Using Extended Kalman Filters’, IEEE Trans. Ind. Electron., 54(1), 272–280, 2007. https://doi.org/10.1109/TIE.200 6.885123.
  • S. Haykin, Adaptive filter theory (3rd ed.). Prentice-Hall, Inc., Upper Saddle River, NJ, USA, 1996.
  • R. Yildiz, R. Demir, and M. Barut, Speed-sensorless predictive torque control of IM based on the adaptive fading extended Kalman filter. IV. International Turkic World Congress on Science and Engineering, pp. 82–93. Niğde, Turkey, 2022.
  • M. Habibullah, D. D. C. Lu, D. Xiao, J. E. Fletcher, and M. F. Rahman, ‘Predictive Torque Control of Induction Motor Sensorless Drive Fed by a 3L-NPC Inverter’, IEEE Trans. Ind. Inform., 13(1), 60–70, 2017. https:// doi.org/10.1109/TII.2016.2603922.

Ayrıntılar

Birincil Dil İngilizce
Konular Mühendislik, Elektrik ve Elektronik
Bölüm Elektrik Elektronik Mühendisliği
Yazarlar

Rıdvan DEMİR> (Sorumlu Yazar)
Kayseri Üniversitesi Mühendislik, Mimarlık ve Tasarım Fakültesi, Elektrik Elektronik Mühendisliği Bölümü
0000-0001-6509-9169
Türkiye


Recep YILDIZ>
NİĞDE ÖMER HALİSDEMİR ÜNİVERSİTESİ, MÜHENDİSLİK FAKÜLTESİ
0000-0002-8167-321X
Türkiye


Murat BARUT>
NİĞDE ÖMER HALİSDEMİR ÜNİVERSİTESİ, MÜHENDİSLİK FAKÜLTESİ
0000-0001-6798-0654
Türkiye

Yayımlanma Tarihi 15 Ocak 2023
Gönderilme Tarihi 22 Kasım 2022
Kabul Tarihi 26 Aralık 2022
Yayınlandığı Sayı Yıl 2023, Cilt 12, Sayı 1

Kaynak Göster

Bibtex @araştırma makalesi { ngumuh1208031, journal = {Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi}, eissn = {2564-6605}, address = {Niğde Üniversitesi Mühendislik Fakültesi, Merkez Yerleşke, Niğde}, publisher = {Niğde Ömer Halisdemir Üniversitesi}, year = {2023}, volume = {12}, number = {1}, pages = {126 - 133}, doi = {10.28948/ngumuh.1208031}, title = {Speed-sensorless predictive torque control of the IM based on MRAS}, key = {cite}, author = {Demir, Rıdvan and Yıldız, Recep and Barut, Murat} }
APA Demir, R. , Yıldız, R. & Barut, M. (2023). Speed-sensorless predictive torque control of the IM based on MRAS . Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi , 12 (1) , 126-133 . DOI: 10.28948/ngumuh.1208031
MLA Demir, R. , Yıldız, R. , Barut, M. "Speed-sensorless predictive torque control of the IM based on MRAS" . Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi 12 (2023 ): 126-133 <https://dergipark.org.tr/tr/pub/ngumuh/issue/75304/1208031>
Chicago Demir, R. , Yıldız, R. , Barut, M. "Speed-sensorless predictive torque control of the IM based on MRAS". Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi 12 (2023 ): 126-133
RIS TY - JOUR T1 - ASM’nin MUS tabanlı hız-algılayıcısız öngörülü moment kontrolü AU - RıdvanDemir, RecepYıldız, MuratBarut Y1 - 2023 PY - 2023 N1 - doi: 10.28948/ngumuh.1208031 DO - 10.28948/ngumuh.1208031 T2 - Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi JF - Journal JO - JOR SP - 126 EP - 133 VL - 12 IS - 1 SN - -2564-6605 M3 - doi: 10.28948/ngumuh.1208031 UR - https://doi.org/10.28948/ngumuh.1208031 Y2 - 2022 ER -
EndNote %0 Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi Speed-sensorless predictive torque control of the IM based on MRAS %A Rıdvan Demir , Recep Yıldız , Murat Barut %T Speed-sensorless predictive torque control of the IM based on MRAS %D 2023 %J Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi %P -2564-6605 %V 12 %N 1 %R doi: 10.28948/ngumuh.1208031 %U 10.28948/ngumuh.1208031
ISNAD Demir, Rıdvan , Yıldız, Recep , Barut, Murat . "Speed-sensorless predictive torque control of the IM based on MRAS". Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi 12 / 1 (Ocak 2023): 126-133 . https://doi.org/10.28948/ngumuh.1208031
AMA Demir R. , Yıldız R. , Barut M. Speed-sensorless predictive torque control of the IM based on MRAS. NÖHÜ Müh. Bilim. Derg.. 2023; 12(1): 126-133.
Vancouver Demir R. , Yıldız R. , Barut M. Speed-sensorless predictive torque control of the IM based on MRAS. Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi. 2023; 12(1): 126-133.
IEEE R. Demir , R. Yıldız ve M. Barut , "Speed-sensorless predictive torque control of the IM based on MRAS", Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi, c. 12, sayı. 1, ss. 126-133, Oca. 2023, doi:10.28948/ngumuh.1208031

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