Araştırma Makalesi
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Pipelining Strategies and Design Considerations of Predictive Current Control Method

Yıl 2022, Cilt: 9 Sayı: 1, 241 - 252, 31.01.2022
https://doi.org/10.31202/ecjse.961021

Öz

This paper explores the pipelining strategies for the model predictive control methods. The array and vector processing methods are examined to discover their applicability in the model predictive current method. The potential benefits of the pipelining methods are investigated, and their design methodologies are scrutinized. The model predictive control is a nonlinear control technique that predicts the system dynamics. The model predictive control (MPC) provides rapid response to the load variations and guarantees robust operation. However, the lower sampling period is the main design constraint to achieve a reliable system operation. The selection of a low sampling period demands a powerful digital controller due to the increasing computational burden. To handle the high calculation burden, a field-programmable gate array (FPGA) is a powerful solution. A proper pipelining strategy enables the use of the MPC in real-time applications. In this paper, pipelining strategies and practical design considerations of the FPGA-based predictive current method are presented. The nine switch converter (NSC) is selected as an experimental case study. The experimental results are provided to demonstrate the theoretical framework. The experimental results prove the feasibility of the array processing and vector processing methods in MPC applications.

Destekleyen Kurum

TUBITAK

Proje Numarası

117E769

Kaynakça

  • [1] I. Gonzalez-Prieto, I. Zoric, M. J. Duran, and E. Levi, “Constrained Model Predictive Control in Nine-Phase Induction Motor Drives,” IEEE Trans. Energy Convers., vol. 34, no. 4, pp. 1881–1889, Dec. 2019.
  • [2] R. E. Perez-Guzman, M. Rivera, and P. W. Wheeler, “Recent Advances of Predictive Control in Power Converters,” in 2020 IEEE International Conference on Industrial Technology (ICIT), 2020, pp. 1100–1105.
  • [3] G. Pei, L. Li, X. Gao, J. Liu, and R. Kennel, “Predictive Current Trajectory Control for PMSM at Voltage Limit,” IEEE Access, vol. 8, pp. 1670–1679, 2020.
  • [4] M. Gokdag and O. Gulbudak, “Model predictive control of AC-DC matrix converter with unity input power factor,” in 2018 IEEE 12th International Conference on Compatibility, Power Electronics and Power Engineering (CPE-POWERENG 2018), 2018, pp. 1–5.
  • [5] M. Gokdag and O. Gulbudak, “Model Predictive Control for Battery Charger Applications with Active Damping,” in 2019 1st Global Power, Energy and Communication Conference (GPECOM), 2019, pp. 140–145.
  • [6] 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, Feb. 2017.
  • [7] S. Vazquez et al., “Model Predictive Control: A Review of Its Applications in Power Electronics,” IEEE Ind. Electron. Mag., vol. 8, no. 1, pp. 16–31, Mar. 2014.
  • [8] M. Siami, D. A. Khaburi, M. Rivera, and J. Rodriguez, “A Computationally Efficient Lookup Table Based FCS-MPC for PMSM Drives Fed by Matrix Converters,” IEEE Trans. Ind. Electron., vol. 64, no. 10, pp. 7645–7654, Oct. 2017.
  • [9] P. Karamanakos, T. Geyer, and R. P. Aguilera, “Long-Horizon Direct Model Predictive Control: Modified Sphere Decoding for Transient Operation,” IEEE Trans. Ind. Appl., vol. 54, no. 6, pp. 6060–6070, Nov. 2018.
  • [10] O. Gulbudak and M. Gokdag, “Asymmetrical Multi-Step Direct Model Predictive Control of Nine-Switch Inverter for Dual-Output Mode Operation,” IEEE Access, vol. 7, pp. 164720–164733, 2019.
  • [11] O. Gulbudak and E. Santi, “FPGA-Based Model Predictive Controller for Direct Matrix Converter,” IEEE Trans. Ind. Electron., vol. 63, no. 7, pp. 4560–4570, Jul. 2016.
  • [12] T. J. Vyncke, S. Thielemans, and J. A. Melkebeek, “Finite-Set Model-Based Predictive Control for Flying-Capacitor Converters: Cost Function Design and Efficient FPGA Implementation,” IEEE Trans. Ind. Informatics, vol. 9, no. 2, pp. 1113–1121, May 2013.
  • [13] Z. Zhang, F. Wang, T. Sun, J. Rodriguez, and R. Kennel, “FPGA-Based Experimental Investigation of a Quasi-Centralized Model Predictive Control for Back-to-Back Converters,” IEEE Trans. Power Electron., vol. 31, no. 1, pp. 662–674, Jan. 2016.
  • [14] F. Wang, L. He, and J. Rodriguez, “FPGA-Based Continuous Control Set Model Predictive Current Control for PMSM System Using Multistep Error Tracking Technique,” IEEE Trans. Power Electron., vol. 35, no. 12, pp. 13455–13464, Dec. 2020.
  • [15] O. Gulbudak and E. Santi, “Model predictive control of dual-output nine-switch inverter with output filter,” in 2015 IEEE Energy Conversion Congress and Exposition (ECCE), 2015, pp. 1582–1589.
  • [16] O. Gulbudak and M. Gokdag, “Predictive dual-induction machine control using nine-switch inverter for multi-drive systems,” in 2018 IEEE 12th International Conference on Compatibility, Power Electronics and Power Engineering (CPE-POWERENG 2018), 2018, pp. 1–6.

Tahmin Akım Kontrol Metodu için Paralel Hesaplama Teknikleri ve Tasarım Kriterleri

Yıl 2022, Cilt: 9 Sayı: 1, 241 - 252, 31.01.2022
https://doi.org/10.31202/ecjse.961021

Öz

Bu makale, model tahmin kontrol metodları için paralel hesaplama tekniklerini araştırır. Dizi işleme ve vektör işleme metodlarının model tahmin akım kontrol metodu uygulamarında kullanılabilirliği araştırılmıştır. Paralel hesaplama metodlarının potensiyel yararları incelenerek, tasarım metodolojileri mercek altına alınmıştır. Model tahmin kontrol metodu, sistem dinamiklerini tahmin eden doğrusal olmayan bir kontrol yöntemidir. Model tahmin kontrol (MTK) metodu yük varyasyonlarına karşı hızlı bir kapalı-çevrim cevabı sağlar, ayrıca güvenilir bir sistem operasyonunu garanti eder. Ancak, kararlı bir operasyonu elde edebilmek için düşük bir örnekleme zamanı seçilmesi gerekmektedir. Örnekleme zamanının düşük seçilmesi, hesaplama yükünü arttıracağı için yüksek performansa sahip bir gömülü sistem işlemcisine ihtiyaç duyulmaktadır. Bu yüksek hesaplama yükünün tolere edilebilmesi için FPGA cihazları güçlü bir çözüm sunmaktadır. İyi tasarlanmış bir paralel hesaplama mimarisi, MTK metodunun gerçek-zamanlı uygulamalarda kullanılabilmesini sağlar. Bu makalede, FPGA-tabanlı model tahmin akım kontrolü için paralel hesaplama mimarileri ve tasarım kriterleri sunulmuştur. Dokuz anahtarlı konvertör (DAK), deneysel uygulamalar için test senaryosu olarak seçilmiştir. Deneysel sonuçlar çalışmada sunulan teorik konsepti desteklenmektedir. Deneysel sonuçlar dizi işleme ve vektör işleme metodunun MTK kontrol yönteminde kullanılabileceğini ispatlamaktadır.

Proje Numarası

117E769

Kaynakça

  • [1] I. Gonzalez-Prieto, I. Zoric, M. J. Duran, and E. Levi, “Constrained Model Predictive Control in Nine-Phase Induction Motor Drives,” IEEE Trans. Energy Convers., vol. 34, no. 4, pp. 1881–1889, Dec. 2019.
  • [2] R. E. Perez-Guzman, M. Rivera, and P. W. Wheeler, “Recent Advances of Predictive Control in Power Converters,” in 2020 IEEE International Conference on Industrial Technology (ICIT), 2020, pp. 1100–1105.
  • [3] G. Pei, L. Li, X. Gao, J. Liu, and R. Kennel, “Predictive Current Trajectory Control for PMSM at Voltage Limit,” IEEE Access, vol. 8, pp. 1670–1679, 2020.
  • [4] M. Gokdag and O. Gulbudak, “Model predictive control of AC-DC matrix converter with unity input power factor,” in 2018 IEEE 12th International Conference on Compatibility, Power Electronics and Power Engineering (CPE-POWERENG 2018), 2018, pp. 1–5.
  • [5] M. Gokdag and O. Gulbudak, “Model Predictive Control for Battery Charger Applications with Active Damping,” in 2019 1st Global Power, Energy and Communication Conference (GPECOM), 2019, pp. 140–145.
  • [6] 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, Feb. 2017.
  • [7] S. Vazquez et al., “Model Predictive Control: A Review of Its Applications in Power Electronics,” IEEE Ind. Electron. Mag., vol. 8, no. 1, pp. 16–31, Mar. 2014.
  • [8] M. Siami, D. A. Khaburi, M. Rivera, and J. Rodriguez, “A Computationally Efficient Lookup Table Based FCS-MPC for PMSM Drives Fed by Matrix Converters,” IEEE Trans. Ind. Electron., vol. 64, no. 10, pp. 7645–7654, Oct. 2017.
  • [9] P. Karamanakos, T. Geyer, and R. P. Aguilera, “Long-Horizon Direct Model Predictive Control: Modified Sphere Decoding for Transient Operation,” IEEE Trans. Ind. Appl., vol. 54, no. 6, pp. 6060–6070, Nov. 2018.
  • [10] O. Gulbudak and M. Gokdag, “Asymmetrical Multi-Step Direct Model Predictive Control of Nine-Switch Inverter for Dual-Output Mode Operation,” IEEE Access, vol. 7, pp. 164720–164733, 2019.
  • [11] O. Gulbudak and E. Santi, “FPGA-Based Model Predictive Controller for Direct Matrix Converter,” IEEE Trans. Ind. Electron., vol. 63, no. 7, pp. 4560–4570, Jul. 2016.
  • [12] T. J. Vyncke, S. Thielemans, and J. A. Melkebeek, “Finite-Set Model-Based Predictive Control for Flying-Capacitor Converters: Cost Function Design and Efficient FPGA Implementation,” IEEE Trans. Ind. Informatics, vol. 9, no. 2, pp. 1113–1121, May 2013.
  • [13] Z. Zhang, F. Wang, T. Sun, J. Rodriguez, and R. Kennel, “FPGA-Based Experimental Investigation of a Quasi-Centralized Model Predictive Control for Back-to-Back Converters,” IEEE Trans. Power Electron., vol. 31, no. 1, pp. 662–674, Jan. 2016.
  • [14] F. Wang, L. He, and J. Rodriguez, “FPGA-Based Continuous Control Set Model Predictive Current Control for PMSM System Using Multistep Error Tracking Technique,” IEEE Trans. Power Electron., vol. 35, no. 12, pp. 13455–13464, Dec. 2020.
  • [15] O. Gulbudak and E. Santi, “Model predictive control of dual-output nine-switch inverter with output filter,” in 2015 IEEE Energy Conversion Congress and Exposition (ECCE), 2015, pp. 1582–1589.
  • [16] O. Gulbudak and M. Gokdag, “Predictive dual-induction machine control using nine-switch inverter for multi-drive systems,” in 2018 IEEE 12th International Conference on Compatibility, Power Electronics and Power Engineering (CPE-POWERENG 2018), 2018, pp. 1–6.
Toplam 16 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Mühendislik
Bölüm Makaleler
Yazarlar

Ozan Gülbudak 0000-0001-9517-3630

Mustafa Gökdağ 0000-0001-5589-2278

Proje Numarası 117E769
Yayımlanma Tarihi 31 Ocak 2022
Gönderilme Tarihi 2 Temmuz 2021
Kabul Tarihi 30 Eylül 2021
Yayımlandığı Sayı Yıl 2022 Cilt: 9 Sayı: 1

Kaynak Göster

IEEE O. Gülbudak ve M. Gökdağ, “Pipelining Strategies and Design Considerations of Predictive Current Control Method”, ECJSE, c. 9, sy. 1, ss. 241–252, 2022, doi: 10.31202/ecjse.961021.