Authors

Alejandro Garcés-Ruiz, Walter Julián Gil González

Abstract

Context: This study focuses on advanced control techniques for permanent magnet synchronous machines (PMSMs), which are crucial in various industrial applications due to their efficiency and precise control requirements. Passivity-based control methods offer stability and performance, addressing these challenges effectively. Method: A passivity-based model predictive control (MPC) is proposed, integrating port-Hamiltonian representation with optimization. Stability theorems are theoretically explored. The simulation evaluates the performance of our proposal under different prediction horizons and stability constraints. Results: The proposed MPC is analyzed across several horizons, both including and excluding passivity and exponential stability constraints.  Conclusions: This study presents a novel passivity-based MPC approach for PMSM speed regulation, highlighting the importance of stability constraints. Future research should extend this controller to synchronous machines in power systems and voltage source converters.

Citation

  • Journal: Ingeniería
  • Year: 2024
  • Volume: 29
  • Issue: 3
  • Pages: e22162
  • Publisher: Universidad Distrital Francisco Jose de Caldas
  • DOI: 10.14483/23448393.22162

BibTeX

@article{Garc_s_Ruiz_2024,
  title={{Passivity-Based Model-Predictive Control for the Permanent Magnet Synchronous Machine}},
  volume={29},
  ISSN={0121-750X},
  DOI={10.14483/23448393.22162},
  number={3},
  journal={Ingeniería},
  publisher={Universidad Distrital Francisco Jose de Caldas},
  author={Garcés-Ruiz, Alejandro and Gil González, Walter Julián},
  year={2024},
  pages={e22162}
}

Download the bib file

References

  • Yaramasu, V., Wu, B., Sen, P. C., Kouro, S. & Narimani, M. High-power wind energy conversion systems: State-of-the-art and emerging technologies. Proceedings of the IEEE vol. 103 740–788 (2015) – 10.1109/jproc.2014.2378692
  • Sami, I. et al. Control Methods for Standalone and Grid Connected Micro-Hydro Power Plants With Synthetic Inertia Frequency Support: A Comprehensive Review. IEEE Access vol. 8 176313–176329 (2020) – 10.1109/access.2020.3026492
  • Ramirez, D., Bartolome, J. P., Martinez, S., Herrero, L. C. & Blanco, M. Emulation of an OWC Ocean Energy Plant With PMSG and Irregular Wave Model. IEEE Transactions on Sustainable Energy vol. 6 1515–1523 (2015) – 10.1109/tste.2015.2455333
  • Kaarthik, R. S., Amitkumar, K. S. & Pillay, P. Emulation of a Permanent-Magnet Synchronous Generator in Real-Time Using Power Hardware-in-the-Loop. IEEE Transactions on Transportation Electrification vol. 4 474–482 (2018) – 10.1109/tte.2017.2778149
  • Hu, K.-W. & Liaw, C.-M. Incorporated Operation Control of DC Microgrid and Electric Vehicle. IEEE Transactions on Industrial Electronics vol. 63 202–215 (2016) – 10.1109/tie.2015.2480750
  • Belkhier, Y. et al. Interconnection and damping assignment passivity-based non-linear observer control for efficiency maximization of permanent magnet synchronous motor. Energy Reports vol. 8 1350–1361 (2022) – 10.1016/j.egyr.2021.12.057
  • Liu, X., Yu, H., Yu, J. & Zhao, Y. A Novel Speed Control Method Based on Port-Controlled Hamiltonian and Disturbance Observer for PMSM Drives. IEEE Access vol. 7 111115–111123 (2019) – 10.1109/access.2019.2934987
  • Ortega, R. & García-Canseco, E. Interconnection and Damping Assignment Passivity-Based Control: A Survey. European Journal of Control vol. 10 432–450 (2004) – 10.3166/ejc.10.432-450
  • Vazquez, S. et al. Model Predictive Control: A Review of Its Applications in Power Electronics. IEEE Industrial Electronics Magazine vol. 8 16–31 (2014) – 10.1109/mie.2013.2290138
  • Schwenzer, M., Ay, M., Bergs, T. & Abel, D. Review on model predictive control: an engineering perspective. The International Journal of Advanced Manufacturing Technology vol. 117 1327–1349 (2021) – 10.1007/s00170-021-07682-3
  • Khanchoul, M., Hilairet, M. & Normand-Cyrot, D. IDA-PBC under sampling for torque control of PMSM. IFAC Proceedings Volumes vol. 46 15–20 (2013) – 10.3182/20130703-3-fr-4038.00059
  • Gil-Gonzalez, W., Garces, A. & Fosso, O. B. Passivity-Based Control for Small Hydro-Power Generation With PMSG and VSC. IEEE Access vol. 8 153001–153010 (2020) – 10.1109/access.2020.3018027
  • Wang, W., Shen, H., Hou, L. & Gu, H. ${H_\infty}$ Robust Control of Permanent Magnet Synchronous Motor Based on PCHD. IEEE Access vol. 7 49150–49156 (2019) – 10.1109/access.2019.2893243
  • Ramírez-Leyva, F. H., Peralta-Sánchez, E., Vásquez-Sanjuan, J. J. & Trujillo-Romero, F. Passivity-Based Speed Control for Permanent Magnet Motors. Procedia Technology vol. 7 215–222 (2013) – 10.1016/j.protcy.2013.04.027
  • Aijaz, M. & Sakthivel, K. Neural Network Based Voltage Source Converter for Power Management of Hybrid Energy System. 2024 Third International Conference on Intelligent Techniques in Control, Optimization and Signal Processing (INCOS) 1–7 (2024) doi:10.1109/incos59338.2024.10527574 – 10.1109/incos59338.2024.10527574
  • Cao, Y. & Guo, J. Research on Characteristic Model-based Adaptive Control of High-speed Permanent Magnet Synchronous Motor With Time Delay. International Journal of Control, Automation and Systems vol. 22 460–474 (2024) – 10.1007/s12555-021-0968-1
  • Zhang, Y. et al. Backstepping control of permanent magnet synchronous motors based on load adaptive fuzzy parameter online tuning. Journal of Power Electronics vol. 24 1059–1070 (2024) – 10.1007/s43236-024-00790-9
  • Yin, Z. et al. Plant-Physics-Guided Neural Network Control for Permanent Magnet Synchronous Motors. IEEE Journal of Selected Topics in Signal Processing vol. 19 74–87 (2025) – 10.1109/jstsp.2024.3430822
  • Sun, W., Si, H., Qiu, J. & Li, J. Research on Efficiency of Permanent-Magnet Synchronous Motor Based on Adaptive Algorithm of Fuzzy Control. Sustainability vol. 16 1253 (2024) – 10.3390/su16031253
  • Li, K., Ding, J., Sun, X. & Tian, X. Overview of Sliding Mode Control Technology for Permanent Magnet Synchronous Motor System. IEEE Access vol. 12 71685–71704 (2024) – 10.1109/access.2024.3402983
  • Huang, Z. et al. Improved Active Disturbance Rejection Control for Permanent Magnet Synchronous Motor. Electronics vol. 13 3023 (2024) – 10.3390/electronics13153023
  • Zhu, J., Duan, Q., Bao, Q. & Mao, Y. Model predictive current control based on hybrid control set for permanent magnet synchronous motor drives. IET Power Electronics vol. 17 450–462 (2024) – 10.1049/pel2.12657
  • Tchoumtcha, D. B., Dagang, C. T. S. & Kenne, G. Synergetic control for stand-alone permanent magnet synchronous generator driven by variable wind turbine. International Journal of Dynamics and Control vol. 12 2888–2902 (2024) – 10.1007/s40435-024-01384-w
  • Chen, L., Liu, D., Sun, L., Zhan, C. & Zhao, J. Sensorless control of permanent magnet synchronous motor based on adaptive enhanced extended state observer. International Journal of Circuit Theory and Applications vol. 52 4303–4322 (2024) – 10.1002/cta.3983
  • Xiao, F., Chen, Z., Chen, Y. & Liu, H. A finite control set model predictive direct speed controller for PMSM application with improved parameter robustness. International Journal of Electrical Power & Energy Systems vol. 143 108509 (2022) – 10.1016/j.ijepes.2022.108509
  • Natarajan, B. et al. Creating Alert Messages Based on Wild Animal Activity Detection Using Hybrid Deep Neural Networks. IEEE Access vol. 11 67308–67321 (2023) – 10.1109/access.2023.3289586
  • Graf, M., Otava, L. & Buchta, L. Simple Linearization Approach for MPC Design for Small PMSM with Field Weakening Performance. IFAC-PapersOnLine vol. 48 159–164 (2015) – 10.1016/j.ifacol.2015.07.025
  • Li, Y., Zhao, C., Zhou, Y. & Qin, Y. Model predictive torque control of PMSM based on data drive. Energy Reports vol. 6 1370–1376 (2020) – 10.1016/j.egyr.2020.11.019
  • T. Raff, C. Ebenbauer, and P. Allgower, Nonlinear Model Predictive Control: A Passivity-Based Approach. Berlin, Heidelberg, Germany: Springer, 2007.
  • Biegler, L. T. A perspective on nonlinear model predictive control. Korean Journal of Chemical Engineering vol. 38 1317–1332 (2021) – 10.1007/s11814-021-0791-7
  • Falugi, P. Model predictive control: a passive scheme. IFAC Proceedings Volumes vol. 47 1017–1022 (2014) – 10.3182/20140824-6-za-1003.02165
  • Tahirovic, A. & Magnani, G. Some Limitations and Real-Time Implementation. SpringerBriefs in Electrical and Computer Engineering 41–51 (2013) doi:10.1007/978-1-4471-5049-7_4 – 10.1007/978-1-4471-5049-7_4
  • van der Schaft, A. & Jeltsema, D. Port-Hamiltonian Systems Theory: An Introductory Overview. (2014) doi:10.1561/9781601987877 – 10.1561/9781601987877
  • Mayne, D. Q., Rawlings, J. B., Rao, C. V. & Scokaert, P. O. M. Constrained model predictive control: Stability and optimality. Automatica vol. 36 789–814 (2000) – 10.1016/s0005-1098(99)00214-9
  • Haddad, W. M. & Chellaboina, V. Nonlinear Dynamical Systems and Control. (2008) doi:10.1515/9781400841042 – 10.1515/9781400841042
  • Andersson, J. A. E., Gillis, J., Horn, G., Rawlings, J. B. & Diehl, M. CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation vol. 11 1–36 (2018) – 10.1007/s12532-018-0139-4