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Predictive current control for permanent magnet synchronous motor based on internal model control observer

Treść / Zawartość
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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The model predictive current control (MPCC) of the permanent magnet synchronous motor (PMSM) is highly dependent on motor parameters, and a parameter mismatch will cause the system performance degradation. Therefore, a strategy based on an internal model control (IMC) observer is proposed to correct the mismatch parameters. Firstly, based on the MPCC strategy of the PMSM, according to the dynamic model of the PMSM in a rotating orthogonal coordinate system, 𝑑-axis and 𝑞-axis current IMC observers are designed, and the stability derivation is carried out. It is proved that the observer can estimate 𝑑-axis and 𝑞-axis disturbance components caused by a parameter mismatch without static error. Then, the estimated disturbance component is compensated for by the reference voltage prediction expression. Finally, the effectiveness of the proposed strategy is verified in two different conditions. The experimental results show that the proposed control strategy can effectively compensate for the parameter mismatch disturbance in MPCC for PMSM, improve the dynamic and static performance of the system, and improve the robustness of the system. voltage prediction expression. Finally, the effectiveness of the proposed strategy is verified in two different conditions. The experimental results show that the proposed control strategy can effectively compensate for the parameter mismatch disturbance in MPCC for PMSM, improve the dynamic and static performance of the system, and improve the robustness of the system.
Rocznik
Strony
343--362
Opis fizyczny
Bibliogr. 21 poz., rys., tab., wz.
Twórcy
autor
  • School of Automation and Electrical Engineering, Lanzhou Jiaotong University China No. 88, Anning West Road, Anning District, Lanzhou City, Gansu Province, China
autor
  • School of Automation and Electrical Engineering, Lanzhou Jiaotong University China No. 88, Anning West Road, Anning District, Lanzhou City, Gansu Province, China
autor
  • School of Automation and Electrical Engineering, Lanzhou Jiaotong University China No. 88, Anning West Road, Anning District, Lanzhou City, Gansu Province, China
Bibliografia
  • [1] Lyskawinski W., Comparative analysis of energy performance of squirrel cage induction motor, linestart synchronous reluctance and permanent magnet motors employing the same stator design, Archives of Electrical Engineering, vol. 69, no. 4, pp. 967–981 (2020), DOI: 10.24425/aee.2020.134642.
  • [2] Pałka R., Piotuch R., Experimental verification of Dead-Beat predictive current controller for small power, low speed PMSM, Archives of Electrical Engineering, vol. 67, no. 2, pp. 333–343 (2018), DOI: 10.24425/119644.
  • [3] Białoń T., Lewicki A., Pasko M., Niestrój R., Non-proportional full-order Luenberger observers of induction motors, Archives of Electrical Engineering, vol. 67, no. 4, pp. 925–937 (2018), DOI: 10.24425/aee.2018.124750.
  • [4] Li Y.H., Qin H., Su J.S., Qin Y.G., Zhao C.H., Zhou Y.F., Model predictive torque control of permanent magnet synchronous motor based on adaptive dynamic weight coefficient using fuzzy control, Electric Machines and Control, vol. 25, no. 2, pp. 102–112 (2021), DOI: 10.15938/j.emc.2021.02.012.
  • [5] Yu F., Zhu C.G.,Wu X.X., Zhang L., Two-vector-based model predictive flux control of three-level based permanent magnet synchronous motor with sector subregion, Transactions of China Electrotechnical Society, vol. 35, no. 10, pp. 2130–2140 (2020), DOI: 10.19595/j.cnki.1000-6753.tces.190520.
  • [6] Xu Y.P., Wang J.B., Zhang B.C., Zhou Q., Three-vector-based model predictive current control for permanent magnet synchronous motor, Transactions of China Electrotechnical Society, vol. 33, no. 5, pp. 980–988 (2018), DOI: 10.19595/j.cnki.1000-6753.tces.170044.
  • [7] Zhang H., Zhang Y.C., Liu J.L., Gao S.Y., Model-free predictive current control of permanent magnet synchronous motor based on single current sampling, Transactions of China Electrotechnical Society, vol. 32, no. 2, pp. 180–187 (2017), DOI: 10.19595/j.cnki.1000-6753.tces.2017.02.021.
  • [8] Elumalai V.K., Subramanian R.G., Reddipogu J.S.D., Enhanced IMC synthesis for tracking control of magnetic levitation system, Archives of Electrical Engineering, vol. 67, no. 2, pp. 293–306 (2018), DOI: 10.24425/119641.
  • [9] Ogbuka C., Nwosu C., Agu M., Dynamic and steady state performance comparison of line-start permanent magnet synchronous motors with interior and surface rotor magnets, Archives of Electrical Engineering, vol. 65, no. 1, pp. 105–116 (2016), DOI: 10.1515/aee-2016-0008.
  • [10] Rolek J., Utrata G., Kaplon A., Robust speed estimation of an induction motor under the conditions of rotor time constant variability due to the rotor deep-bar effect, Archives of Electrical Engineering, vol. 69, no. 2, pp. 319–333 (2020), DOI: 10.24425/aee.2020.133028.
  • [11] Verrelli C.M., Savoia A., Mengoni M., Marino R., Tomei P., Zarri L., On-line identification of winding resistances and load torque in induction machines, IEEE Transactions on Control Systems Technology, vol. 22, no. 4, pp. 1629–1637 (2014), DOI: 10.1109/TCST.2013.2285604.
  • [12] Jin B.L., Shen Y.X., Wu D.H., Permanent magnet synchronous motor parameter identification with multi-innovation least squares, Proceedings of the 2016 IEEE 11th Conference on Industrial Electronics and Applications, Wuxi, China, pp. 752–757 (2016).
  • [13] Chen H., Hao R.X., Liu Y.Y., Wang H., Wang T.X., Li D.L., Parameter identification of time-varying exponential load model based on improved RLS algorithm, High Voltage Engineering, vol. 46, no. 7, pp. 2380–2388 (2020), DOI: 10.13336/j.1003-6520.hve.20200310013.
  • [14] Xie S.W., Xie Y.F., Huang T.W., Gui W.H., Yang C.H., Generalized predictive control for industrial processes based on neuron adaptive splitting and merging RBF neural network, IEEE Transactions on Industrial Electronics, vol. 66, no. 2, pp. 1192–1202 (2019), DOI: 10.1109/TIE.2018.2835402.
  • [15] Fan Z.F., Ma X.P., Shao X.G., Method to determine initial value of local optimization forneural network predictive control, Control Theory and Applications, vol. 31, no. 6, pp. 741–747 (2014), DOI: 10.7641/CTA.2014.31262.
  • [16] Yuan L., Wen T.S., Sen C., Jun F.J., Research and application of predictive control algorithm based on fuzzy model, 2015 International Conference on Advanced Mechatronic Systems, Beijing, China, pp. 244–248 (2015).
  • [17] Seddjar A., Kerrouche K.D.E., Wang L., Simulation of the proposed combined Fuzzy Logic Control for Maximum Power Point Tracking and Battery Charge Regulation used in Cube Sat, Archives of Electrical Engineering, vol. 69, no. 3, pp. 521–543 (2020), DOI: 10.24425/aee.2020.133916.
  • [18] Qin Y.Z., Yan Y., Chen W., Geng Q., Three-vector model predictive current control strategy for permanent magnet synchronous motor drives with parameter error compensation, Transactions of China Electrotechnical Society, vol. 35, no. 2, pp. 255–265 (2020), DOI: 10.19595/j.cnki.1000-6753.tces.181693.
  • [19] Siami M., Khaburi D.A., Rodriguez J., Torque ripple reduction of predictive torque control for PMSM drives with parameter mismatch, IEEE Transactions on Power Electronics, vol. 32, no. 9, pp. 7160–7168 (2017), DOI: 10.1109/TPEL.2016.2630274.
  • [20] Huang Y.W., Xiong S.H., An internal model control-based observer for current loops in permanent magnet synchronous motor, Proceedings of the CSEE, vol. 36, no. 11, pp. 3070–3075 (2016), DOI: 10.13334/j.0258-8013.pcsee.2016.11.024.
  • [21] Jia Q., Xia C., Exponential stability of impulsive delayed nonlinear hybrid differential systems, Archives of Electrical Engineering, vol. 68, no. 3, pp. 553–564 (2019), DOI: 10.24425/aee.2019.129341
Uwagi
Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-4575f392-8240-49b0-a8e7-4c3a8d2627fe
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