Model Predictive Control of LC Filter Equipped Surface-Mounted PMSM Drives Using Exact Discretization

In this article, a novel method is presented to exactly discretize the state-space model of surface-mounted permanent magnet synchronous machines (PMSMs) that are equipped with an inductive-capacitive (LC) filter to allow the model predictive control of the system. An approach is presented to calculate the matrix exponential and the inverse of the angular frequency dependent state-matrix required for the exact discretization. This enables real-time implementation without using memory extensive lookup tables. The method is compared with the Taylor series up to the third term and bilinear approximations as well. Measurement comparatively verifies the theoretical findings in steady-state at various sampling rates, computational requirements, and prediction accuracy experiments. Furthermore, the robustness of the system to speed reference, load torque, and parameter change transients is also explored.

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Publication Details

Journal
IEEE Transactions on Industrial Electronics
Published
2024-08-21
DOI
https://doi.org/10.1109/tie.2024.3436637
Citations
4
Primary Topic
Iterative Learning Control Systems
Type
article
Field-Weighted Citation Impact
1.31
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article

Model Predictive Control of LC Filter Equipped Surface-Mounted PMSM Drives Using Exact Discretization

Péter Stumpf, Kristóf Bándy
4 citations
IEEE Transactions on Industrial Electronics
Iterative Learning Control Systems
1.31
article

Model Predictive Control of LC Filter Equipped Surface-Mounted PMSM Drives Using Exact Discretization

Péter Stumpf, Kristóf Bándy
article en
4 citations

Abstract

In this article, a novel method is presented to exactly discretize the state-space model of surface-mounted permanent magnet synchronous machines (PMSMs) that are equipped with an inductive-capacitive (LC) filter to allow the model predictive control of the system. An approach is presented to calculate the matrix exponential and the inverse of the angular frequency dependent state-matrix required for the exact discretization. This enables real-time implementation without using memory extensive lookup tables. The method is compared with the Taylor series up to the third term and bilinear approximations as well. Measurement comparatively verifies the theoretical findings in steady-state at various sampling rates, computational requirements, and prediction accuracy experiments. Furthermore, the robustness of the system to speed reference, load torque, and parameter change transients is also explored.

IEEE Transactions on Industrial ElectronicsVol. 72(3)
Budapest University of Technology and Economics (HU)
Affordable and clean energy
Openalex Percentile: Top 20%
Iterative Learning Control Systems
1.31
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