Research Article
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Modulated Model Predictive Torque Control for Interior Permanent Magnet Synchronous Machines

Year 2022, Volume: 9 Issue: 2, 777 - 787, 31.05.2022
https://doi.org/10.31202/ecjse.1008121

Abstract

Thanks to the advancements in the processor industry, the popularity in the industrial applications of Finite control set model predictive control (FCS-MPC) is increasing. FCS-MPC has several advantages, such as high closed-loop bandwidth, the inclusion of the control constraints, and nonlinearities. However, the control signals are directly produced by the predictive controller since no modulator is used. Hence, the system has non-fixed switching frequency, and the maximum achievable switching frequency is limited by the half of the sampling frequency. However, the control goals may suffer from the undesired ripples in case of a noticeable low switching frequency. To eliminate these ripples the sampling period of the system can be reduced. But this increases the computational burden on the processor. To overcome the unwanted oscillations in the control variables and decrease the computational burden on the processor, a modulated model predictive control (M2PC) strategy is proposed in this paper. The M2PC combines the space vector pulse width modulator (SVPWM) and FCS-MPC. Torque of the interior permanent magnet synchronous motor (IPMSM) is controlled with M2PC method. The motor is controlled in a constant torque region with the combination of the M2PC method and maximum torque per ampere (MTPA) control strategy. The comparative results of the conventional MPC method and M2PC method are reported in the paper and the superiority of the M2PC strategy is validated by simulation works. The results demonstrate that the M2PC method significantly reduces total harmonic distortion (THD) in stator currents. Based on the results, the M2PC method provides a better control performance for IPMSMs with significantly reduced torque ripples.

Supporting Institution

TüBİTAK

Project Number

118E858

Thanks

This study has been supported by the Scientific and Technological Research Council of Turkey (TUBITAK) through the Scientific and Technological Research Projects Funding Program (1001) with a project numbered as 118E858.

References

  • [1]. M. A. H. M.F and S. E., “Permanent Magnet Flux Switching Torque Performance Indicator,” El-Cezeri, vol. 8, no. 2, pp. 582–591, 2021.
  • [2]. B. M. and Yıldırız E., “Constant Current/Voltage Charging of A 250W E-Bike with Wireless Power Transfer,” El-Cezeri, vol. 7, no. 7, pp. 189–197, 2020.
  • [3]. İ. KIYAK and K. Y. KAYA, “Elektrikli Taşıtlarda Kullanılan İndüksiyon / Sabit Mıknatıslı Motor Sürücülerinin Simülasyonu ve Motor Dinamiklerinin Analizi,” Int. J. Adv. Eng. Pure Sci., vol. 32, no. 2, pp. 152–157, 2020.
  • [4]. H. Murakami, Y. Honda, H. Kiriyama, S. Morimoto, and Y. Takeda, “The performance comparison of SPMSM, IPMSM and SynRM in use as air-conditioning compressor,” in Conference Record of the 1999 IEEE Industry Applications Conference. Thirty-Forth IAS Annual Meeting (Cat. No.99CH36370), 1999, vol. 2, pp. 840–845 vol.2.
  • [5]. F. Wang, X. Mei, J. Rodriguez, and R. Kennel, “Model predictive control for electrical drive systems-an overview,” CES Trans. Electr. Mach. Syst., vol. 1, no. 3, pp. 219–230, 2020.
  • [6]. M. F. Elmorshedy, W. Xu, F. F. M. El-Sousy, M. R. Islam, and A. A. Ahmed, “Recent Achievements in Model Predictive Control Techniques for Industrial Motor: A Comprehensive State-of-the-Art,” IEEE Access, vol. 9, pp. 58170–58191, 2021.
  • [7]. S. Vazquez, J. Rodriguez, M. Rivera, L. G. Franquelo, and M. Norambuena, “Model Predictive Control for Power Converters and Drives: Advances and Trends,” IEEE Trans. Ind. Electron., vol. 64, no. 2, pp. 935–947, 2017.
  • [8]. V. Yaramasu, K. Milev, A. Dekka, M. Rivera, J. Rodriguez, and F. Rojas, “Modulated Model Predictive Current Control of a Four-Leg Inverter,” in 2020 11th Power Electronics, Drive Systems, and Technologies Conference (PEDSTC), 2020, pp. 1–6.
  • [9]. K. Li and Y. Wang, “Maximum Torque Per Ampere (MTPA) Control for IPMSM Drives Based on a Variable-Equivalent-Parameter MTPA Control Law,” IEEE Trans. Power Electron., vol. 34, no. 7, pp. 7092–7102, 2019.
  • [10]. O. Gulbudak and M. Gokdag, “FPGA-Based Model Predictive Control for Power Converters,” Proc. - 2020 IEEE 2nd Glob. Power, Energy Commun. Conf. GPECOM 2020, pp. 30–35, 2020.
  • [11]. M. Vijayagopal, P. Zanchetta, L. Empringham, L. de Lillo, L. Tarisciotti, and P. Wheeler, “Control of a Direct Matrix Converter With Modulated Model-Predictive Control,” IEEE Trans. Ind. Appl., vol. 53, no. 3, pp. 2342–2349, 2017.
  • [12]. D. Xiao, K. S. Alam, M. Norambuena, M. F. Rahman, and J. Rodriguez, “Modified Modulated Model Predictive Control Strategy for a Grid-Connected Converter,” IEEE Trans. Ind. Electron., vol. 68, no. 1, pp. 575–585, 2021.
  • [13]. K. Milev, V. Yaramasu, A. Dekka, and S. Kouro, “Modulated Predictive Current Control of PMSG-Based Wind Energy Systems,” in 2020 11th Power Electronics, Drive Systems, and Technologies Conference (PEDSTC), 2020, pp. 1–6.
  • [14]. M. Gokdag and O. Gulbudak, “Imposed Source Current Predictive Control for Battery Charger Applications with Active Damping,” Sak. Univ. J. Sci., vol. 23, no. 5, pp. 964–971, 2019.
  • [15]. M. Rivera et al., “A modulated model predictive control scheme for a two-level voltage source inverter,” Proc. IEEE Int. Conf. Ind. Technol., vol. 2015-June, no. June, pp. 2224–2229, 2015.
  • [16]. J. Zeng, Q. Lin, S. Chen, X. Su, X. Lin, and T. Chen, “A Modulated Model Predictive Control for Three Phase Voltage Source Inverter,” Proc. - 2020 Chinese Autom. Congr. CAC 2020, pp. 2899–2902, 2020.
  • [17]. F. Zhang, T. Peng, H. Dan, J. Lin, and M. Su, “Modulated model predictive control of permanent magnet synchronous motor,” in 2018 IEEE International Conference on Industrial Electronics for Sustainable Energy Systems (IESES), 2018, pp. 130–133.
  • [18]. C. Garcia, J. Rodriguez, S. Odhano, P. Zanchetta, and S. A. Davari, “Modulated Model Predictive Speed Control for PMSM Drives,” 2018 IEEE Int. Conf. Electr. Syst. Aircraft, Railw. Sh. Propuls. Road Veh. Int. Transp. Electrif. Conf. ESARS-ITEC 2018, no. 1, 2019.
  • [19]. Q. Wang, M. Rivera, J. A. Riveros, and P. Wheeler, “Modulated Model Predictive Current Control for PMSM Operating with Three-level NPC Inverter,” 2019 IEEE 15th Brazilian Power Electron. Conf. 5th IEEE South. Power Electron. Conf. COBEP/SPEC 2019, 2019.
  • [20]. J. Rodriguez et al., “Predictive Current Control of a Voltage Source Inverter,” IEEE Trans. Ind. Electron., vol. 54, no. 1, pp. 495–503, 2007.
  • [21]. M. KOÇ, S. EMİROĞLU, and B. TAMYÜREK, “Analysis and simulation of efficiency optimized IPM drives in constant torque region with reduced computational burden,” Turkish J. Electr. Eng. Comput. Sci., vol. 29, no. 3, pp. 1643–1658, 2021.

Gömülü Mıknatıslı Senkron Motorların Modüleli Model Öngörülü Tork Kontrolü

Year 2022, Volume: 9 Issue: 2, 777 - 787, 31.05.2022
https://doi.org/10.31202/ecjse.1008121

Abstract

İşlemci endüstrisindeki ilerlemeler sayesinde Model Öngörülü Kontrol’ün endüstriyel uygulamalarındaki popülaritesi artmaktadır. MÖK’ün yüksek kapalı döngü bant genişliğine sahip olması, kontrol kısıtlamalarının ve doğrusal olmayan durumların kontrole dâhil edilmesi gibi birçok avantajı bulunmaktadır. Fakat modülatör kullanılmadığı için kontrol sinyalleri doğrudan öngörücü kontrolör tarafından üretilmektedir. Bundan dolayı sistem değişken anahtarlama frekansına sahiptir ve maksimum elde edilebilecek frekans örnekleme frekansının yarısı ile sınırlıdır. Bununla birlikte, düşük anahtarlama frekanslarında kontrol değişkenlerinde istenmeyen dalgalanmalar oluşmaktadır. Bu dalgalanmaları elimine etmek için sistemin örnekleme periyodu düşürülebilir. Ama bu işlemcinin üzerindeki matematiksel yükü arttırmaktadır. Kontrol değişkenlerindeki istenmeyen dalgalanmaları azaltmak ve işlemcinin üzerindeki matematiksel yükü hafifletmek için bu çalışmada Modüleli Model Öngörülü Kontrol (MMÖK) stratejisi önerilmiştir. MMÖK metodu MÖK metodu ile uzay vektör darbe genişlik modülasyonun (UVDGM) birleşimidir. Gömülü mıknatıslı senkron motorun (GMSM) torku M2PC metodu ile kontrol edilmiştir. Motor, MMÖK metodu ve akım başına maksimum tork (ABMT) kontrol stratejisinin birleşimiyle sabit tork bölgesinde kontrol edilmiştir. Çalışmada geleneksel MÖK metodu ile MMÖK metodunun sonuçları karşılaştırılmış ve MMÖK kontrol stratejisinin üstünlüğü simülasyon çalışmaları ile doğrulanmıştır. Sonuçlar, MMÖK yönteminin stator akımlarındaki toplam harmonik bozulmayı (THB) önemli ölçüde azalttığını göstermektedir. MMÖK yönteminin torkdaki dalgalanmaları önemli ölçüde azaltarak, GMSM ler için daha iyi kontrol performansı sağladığı simülasyon sonuçları ile doğrulanmıştır.

Project Number

118E858

References

  • [1]. M. A. H. M.F and S. E., “Permanent Magnet Flux Switching Torque Performance Indicator,” El-Cezeri, vol. 8, no. 2, pp. 582–591, 2021.
  • [2]. B. M. and Yıldırız E., “Constant Current/Voltage Charging of A 250W E-Bike with Wireless Power Transfer,” El-Cezeri, vol. 7, no. 7, pp. 189–197, 2020.
  • [3]. İ. KIYAK and K. Y. KAYA, “Elektrikli Taşıtlarda Kullanılan İndüksiyon / Sabit Mıknatıslı Motor Sürücülerinin Simülasyonu ve Motor Dinamiklerinin Analizi,” Int. J. Adv. Eng. Pure Sci., vol. 32, no. 2, pp. 152–157, 2020.
  • [4]. H. Murakami, Y. Honda, H. Kiriyama, S. Morimoto, and Y. Takeda, “The performance comparison of SPMSM, IPMSM and SynRM in use as air-conditioning compressor,” in Conference Record of the 1999 IEEE Industry Applications Conference. Thirty-Forth IAS Annual Meeting (Cat. No.99CH36370), 1999, vol. 2, pp. 840–845 vol.2.
  • [5]. F. Wang, X. Mei, J. Rodriguez, and R. Kennel, “Model predictive control for electrical drive systems-an overview,” CES Trans. Electr. Mach. Syst., vol. 1, no. 3, pp. 219–230, 2020.
  • [6]. M. F. Elmorshedy, W. Xu, F. F. M. El-Sousy, M. R. Islam, and A. A. Ahmed, “Recent Achievements in Model Predictive Control Techniques for Industrial Motor: A Comprehensive State-of-the-Art,” IEEE Access, vol. 9, pp. 58170–58191, 2021.
  • [7]. S. Vazquez, J. Rodriguez, M. Rivera, L. G. Franquelo, and M. Norambuena, “Model Predictive Control for Power Converters and Drives: Advances and Trends,” IEEE Trans. Ind. Electron., vol. 64, no. 2, pp. 935–947, 2017.
  • [8]. V. Yaramasu, K. Milev, A. Dekka, M. Rivera, J. Rodriguez, and F. Rojas, “Modulated Model Predictive Current Control of a Four-Leg Inverter,” in 2020 11th Power Electronics, Drive Systems, and Technologies Conference (PEDSTC), 2020, pp. 1–6.
  • [9]. K. Li and Y. Wang, “Maximum Torque Per Ampere (MTPA) Control for IPMSM Drives Based on a Variable-Equivalent-Parameter MTPA Control Law,” IEEE Trans. Power Electron., vol. 34, no. 7, pp. 7092–7102, 2019.
  • [10]. O. Gulbudak and M. Gokdag, “FPGA-Based Model Predictive Control for Power Converters,” Proc. - 2020 IEEE 2nd Glob. Power, Energy Commun. Conf. GPECOM 2020, pp. 30–35, 2020.
  • [11]. M. Vijayagopal, P. Zanchetta, L. Empringham, L. de Lillo, L. Tarisciotti, and P. Wheeler, “Control of a Direct Matrix Converter With Modulated Model-Predictive Control,” IEEE Trans. Ind. Appl., vol. 53, no. 3, pp. 2342–2349, 2017.
  • [12]. D. Xiao, K. S. Alam, M. Norambuena, M. F. Rahman, and J. Rodriguez, “Modified Modulated Model Predictive Control Strategy for a Grid-Connected Converter,” IEEE Trans. Ind. Electron., vol. 68, no. 1, pp. 575–585, 2021.
  • [13]. K. Milev, V. Yaramasu, A. Dekka, and S. Kouro, “Modulated Predictive Current Control of PMSG-Based Wind Energy Systems,” in 2020 11th Power Electronics, Drive Systems, and Technologies Conference (PEDSTC), 2020, pp. 1–6.
  • [14]. M. Gokdag and O. Gulbudak, “Imposed Source Current Predictive Control for Battery Charger Applications with Active Damping,” Sak. Univ. J. Sci., vol. 23, no. 5, pp. 964–971, 2019.
  • [15]. M. Rivera et al., “A modulated model predictive control scheme for a two-level voltage source inverter,” Proc. IEEE Int. Conf. Ind. Technol., vol. 2015-June, no. June, pp. 2224–2229, 2015.
  • [16]. J. Zeng, Q. Lin, S. Chen, X. Su, X. Lin, and T. Chen, “A Modulated Model Predictive Control for Three Phase Voltage Source Inverter,” Proc. - 2020 Chinese Autom. Congr. CAC 2020, pp. 2899–2902, 2020.
  • [17]. F. Zhang, T. Peng, H. Dan, J. Lin, and M. Su, “Modulated model predictive control of permanent magnet synchronous motor,” in 2018 IEEE International Conference on Industrial Electronics for Sustainable Energy Systems (IESES), 2018, pp. 130–133.
  • [18]. C. Garcia, J. Rodriguez, S. Odhano, P. Zanchetta, and S. A. Davari, “Modulated Model Predictive Speed Control for PMSM Drives,” 2018 IEEE Int. Conf. Electr. Syst. Aircraft, Railw. Sh. Propuls. Road Veh. Int. Transp. Electrif. Conf. ESARS-ITEC 2018, no. 1, 2019.
  • [19]. Q. Wang, M. Rivera, J. A. Riveros, and P. Wheeler, “Modulated Model Predictive Current Control for PMSM Operating with Three-level NPC Inverter,” 2019 IEEE 15th Brazilian Power Electron. Conf. 5th IEEE South. Power Electron. Conf. COBEP/SPEC 2019, 2019.
  • [20]. J. Rodriguez et al., “Predictive Current Control of a Voltage Source Inverter,” IEEE Trans. Ind. Electron., vol. 54, no. 1, pp. 495–503, 2007.
  • [21]. M. KOÇ, S. EMİROĞLU, and B. TAMYÜREK, “Analysis and simulation of efficiency optimized IPM drives in constant torque region with reduced computational burden,” Turkish J. Electr. Eng. Comput. Sci., vol. 29, no. 3, pp. 1643–1658, 2021.
There are 21 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Makaleler
Authors

Uğur Ufuk Körpe 0000-0001-6450-1697

Mustafa Gökdağ 0000-0001-5589-2278

Mikail Koç 0000-0003-1465-1878

Ozan Gülbudak 0000-0001-9517-3630

Project Number 118E858
Publication Date May 31, 2022
Submission Date October 11, 2021
Acceptance Date December 14, 2021
Published in Issue Year 2022 Volume: 9 Issue: 2

Cite

IEEE U. U. Körpe, M. Gökdağ, M. Koç, and O. Gülbudak, “Modulated Model Predictive Torque Control for Interior Permanent Magnet Synchronous Machines”, El-Cezeri Journal of Science and Engineering, vol. 9, no. 2, pp. 777–787, 2022, doi: 10.31202/ecjse.1008121.
Creative Commons License El-Cezeri is licensed to the public under a Creative Commons Attribution 4.0 license.
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