Real-Time Control of an IPMSM Using Model Order Reduction

Mehrnaz Farzam Far*, Floran Martin, Anouar Belachen, Paavo Rasilo, Hafiz Asad Ali Awan

*Corresponding author for this work

    Research output: Contribution to journalArticleScientificpeer-review

    23 Citations (Scopus)

    Abstract

    In the control of electric drives, inaccurate estimation of the motor parameters affects the robustness of the control. This effect is observed particularly when the linear lumped parameter-based models are employed to represent a machine composed of nonlinear magnetic materials. Finite-element (FE) models consider the material nonlinearity accurately. However, implementing these models in a real-time system is challenging due to the computational burden. In this article, we propose a fast-dynamic model, based on a model order reduction method, to control a permanent magnet synchronous machine in a wide range of speed. The stator currents in the rotor frame of reference are given as inputs to a reduced FE model, which computes the nodal values of the magnetic vector potential and thereafter the flux linkages. A discrete-time model is used to control the rotor speed and the stator current components. Experiments on a 2.2 kW interior permanent magnet synchronous machine verify the viability of the proposed model.
    Original languageEnglish
    Article number9007620
    Pages (from-to)2005-2014
    JournalIEEE Transactions on Industrial Electronics
    Volume68
    Issue number3
    Early online date2020
    DOIs
    Publication statusPublished - Mar 2021
    MoE publication typeA1 Journal article-refereed

    Funding

    This work was supported in part by the Estonian Research Council, Tallinn, Estonia, under Grant PUT1260, and in part by the Academy of Finland, Helsinki, Finland, under Grant 287395 and Grant 297345.

    Keywords

    • electrical machine
    • interior permanent magnet
    • model order reduction
    • Orthogonal interpolation method
    • real-time control
    • rotor frame of reference
    • singular value decomposition

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