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Selection of reference model for adaptive PMSM drive based on MRAC approach

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Języki publikacji
EN
Abstrakty
EN
The selection of a reference model (RM) for a Model-Reference Adaptive Control is one of the most important aspects of the synthesis process of the adaptive control system. In this paper, the four different implementations of RM are developed and investigated in an adaptive PMSM drive with variable moment of inertia. Adaptation mechanisms are based on the Widrow-Hoff rule (W-H) and the Adaptation Procedure for Optimization Algorithms (APOA). Inadequate order or inaccurate approximation of RM for the W-H rule may provide poor behavior and oscillations. The results prove that APOA is robust against an improper selection of RM and provides high-performance PMSM drive operation.
Rocznik
Strony
art. no. e149177
Opis fizyczny
Bibliogr. 27 poz., rys., tab.
Twórcy
  • Department of Automatics and Measurement Systems, Nicolaus Copernicus University, Grudziadzka 5, 87-100 Torun, Poland
  • Department of Automatics and Measurement Systems, Nicolaus Copernicus University, Grudziadzka 5, 87-100 Torun, Poland
  • Institute of Control and Industrial Electronics, Warsaw University of Technology, Koszykowa 75, 00-662 Warsaw, Poland
Bibliografia
  • [1] V. Koropouli, A. Gusrialdi, S. Hirche, and D. Lee, “An extremum-seeking control approach for constrained robotic motion tasks,” Control Eng. Pract., vol. 52, pp. 1–14, 2016.
  • [2] T. Tarczewski, Ł.J. Niewiara, and L.M. Grzesiak, “Artificial neural network-based gain-scheduled state feedback speed controller for synchronous reluctance motor,” Power Electron. Drives, vol. 6, 2021.
  • [3] M.Korzonek, G. Tarchala, and T. Orlowska-Kowalska, “A review on mras-type speed estimators for reliable and efficient induction motor drives,” ISA Trans., vol. 93, pp. 1–13, 2019.
  • [4] X. Wang, B. Ufnalski, and L.M. Grzesiak, “Adaptive speed control in the pmsm drive for a non-stationary repetitive process using particle swarms,” in 2016 10th International Conference on Compatibility, Power Electronics and Power Engineering (CPEPOWERENG). IEEE, 2016, pp. 464–471.
  • [5] E. Kilic, H.R. Ozcalik, and S. Yilmaz, “Efficient speed control of induction motor using rbf based model reference adaptive control method,” Automatika: Časopis za Automatiku, Mjerenje, Elektroniku, Računarstvo i komunikacije, vol. 57, no. 3, pp. 714–723, 2016.
  • [6] Z.A. Alrowaili et al., “Robust adaptive hcs mppt algorithm-based wind generation system using model reference adaptive control,” Sensors, vol. 21, no. 15, p. 5187, 2021.
  • [7] K.J. Åström and B. Wittenmark, Adaptive control. Courier Corporation, 2013.
  • [8] R. Szczepanski, T. Tarczewski, and L.M. Grzesiak, “Application of optimization algorithms to adaptive motion control for repetitive process,” ISA Trans., vol. 115, pp. 192–205, 2021.
  • [9] M. Malarczyk, J.-R. Tapamo, and M. Kaminski, “Application of neural data processing in autonomous model platform—a complex review of solutions, design and implementation,” Energies, vol. 15, no. 13, p. 4766, 2022.
  • [10] J.C. Travieso-Torres and M.A. Duarte-Mermoud, “Normalized model reference adaptive control applied to high starting torque scalar control scheme for induction motors,” Energies, vol. 15, no. 10, p. 3606, 2022.
  • [11] H.H. Nguyen, M.T. Tran, D.H. Kim, H.K. Kim, and S.B. Kim, “Velocity controller design for fish sorting belt conveyor system using m-mrac and projection operator,” J. Power Syst. Eng., vol. 21, no. 4, pp. 42–50, 2017.
  • [12] H. Liu, X. Zhang, Y. Chen, M. Taha, and H. Xu, “Active damping of driveline vibration in power-split hybrid vehicles based on model reference control,” Control Eng. Pract., vol. 91, p. 104085, 2019.
  • [13] E. Arabi and T. Yucelen, “A set-theoretic model reference adaptive control architecture with dead-zone effect,” Control Eng. Pract., vol. 89, pp. 12–29, 2019.
  • [14] X. Sun, Y. Zhang, X. Tian, J. Cao, and J. Zhu, “Speed sensorless control for ipmsms using a modified mras with gray wolf optimization algorithm,” IEEE Trans. Transp. Electrif., vol. 8, no. 1, pp. 1326–1337, 2021.
  • [15] M. Öztekin, O. Kiselychnyk, and J. Wang, “Nonlinear optimal control for interior permanent magnet synchronous motor drives,” in 2022 European Control Conference (ECC). IEEE, 2022, pp. 590–595.
  • [16] R. Szczepanski, T. Tarczewski, and L. Grzesiak, “Pmsm drive with adaptive state feedback speed controller,” Bull. Pol. Acad. Sci. Tech. Sci., vol. 68, no. 5, 2020.
  • [17] M. Kamiński, “Zastosowanie algorytmu bat w optymalizacji obliczeń adaptacyjnego regulatora stanu układu dwumasowego,” Przegląd Elektrotechniczny, vol. 93, no. 1, pp. 300–304, 2017, in Polish.
  • [18] R. Szczepanski, T. Tarczewski, and L.M. Grzesiak, “Wybór modelu odniesienia dla adaptacyjnego napędu z silnikiem PMSM bazującego na metodzie MRAC,” in Procedings of conference Sterowanie w Energoelektronice i Napedzie Elektrycznym SENE 2022, 2022, in Polish.
  • [19] L.M. Grzesiak and T. Tarczewski, “Pmsm servo-drive control system with a state feedback and a load torque feedforward compensation,” COMPEL-Int. J. Comput. Math. Electr. Electron. Eng., vol. 32, no. 1, pp. 364–382, 2013.
  • [20] G.F. Franklin et al., Digital control of dynamic systems. Addison-Wesley Reading, MA, 1998, vol. 3.
  • [21] X. Sun, C. Hu, G. Lei, Y. Guo, and J. Zhu, “State feedback control for a pm hub motor based on gray wolf optimization algorithm,” IEEE Trans. Power Electron., vol. 35, no. 1, pp. 1136–1146, 2019.
  • [22] J. Cao, X. Sun, and X. Tian, “Optimal control strategy of state feedback control for surface-mounted pmsm drives based on auto-tuning of seeker optimization algorithm,” Int. J. Appl. Electromagn. Mech., vol. 66, no. 4, pp. 705–725, 2021.
  • [23] T. Tarczewski and L.M. Grzesiak, “Constrained state feedback speed control of pmsm based on model predictive approach,” IEEE Trans. Ind. Electron., vol. 63, no. 6, pp. 3867–3875, 2015.
  • [24] A.F.U. Din et al., “Robust flight control system design of a fixed wing uav using optimal dynamic programming,” Soft Comput., pp. 1–12, 2022.
  • [25] M. Dehghani, Š. Hubálovskỳ, and P. Trojovskỳ, “Cat and mouse based optimizer: a new nature-inspired optimization algorithm,” Sensors, vol. 21, no. 15, p. 5214, 2021.
  • [26] F. Gul et al., “A centralized strategy for multi-agent exploration,” IEEE Access, vol. 10, pp. 126 871–126 884, 2022.
  • [27] F. Gul, W. Rahiman, S. Alhady, A. Ali, I. Mir, and A. Jalil, “Metaheuristic approach for solving multi-objective path planning for autonomous guided robot using pso–gwo optimization algorithm with evolutionary programming,” J. Ambient Intell. Humaniz. Comput., vol. 12, pp. 7873–7890, 2021.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-882526a8-b2a0-4702-83e6-704d783a4cd4
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