Constrained Long-Horizon Direct Model Predictive Control for Synchronous Reluctance Motor Drives

L. Ortombina, E. Liegmann, P. Karamanakos, F. Tinazzi, M. Zigliotto, R. Kennel

    Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

    1 Citation (Scopus)

    Abstract

    A finite control set model predictive control strategy for the control of the stator currents of a synchronous reluctance motor driven by a three-level neutral point clamped inverter is presented in this paper. The presented algorithm minimizes the stator current distortions while operating the drive system at switching frequencies of a few hundred Hertz. Moreover, the power electronic converter is protected by overcurrents and/or overvoltages owing to a hard constraint imposed on the stator currents. To efficiently solve the underlying integer nonlinear optimization problem a sphere decoding algorithm serves as optimizer. To this end, a numerical calculation of the unconstrained solution of the optimization problem is proposed, along with modifications in the algorithm proposed in [1] so as to meet the above-mentioned control objectives. Simulation results show the effectiveness of the proposed control algorithm.

    Original languageEnglish
    Title of host publication2018 IEEE 19th Workshop on Control and Modeling for Power Electronics, COMPEL 2018
    PublisherIEEE
    Number of pages8
    ISBN (Print)9781538655412
    DOIs
    Publication statusPublished - 10 Sep 2018
    Publication typeA4 Article in a conference publication
    EventIEEE Workshop on Control and Modeling for Power Electronics - Padova, Italy
    Duration: 25 Jun 201828 Jun 2018

    Conference

    ConferenceIEEE Workshop on Control and Modeling for Power Electronics
    Country/TerritoryItaly
    CityPadova
    Period25/06/1828/06/18

    Publication forum classification

    • Publication forum level 0

    ASJC Scopus subject areas

    • Modelling and Simulation
    • Energy Engineering and Power Technology
    • Electrical and Electronic Engineering
    • Control and Optimization

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