JACOPO RICCIO JACOPO.RICCIO2@NOTTINGHAM.AC.UK
Research Fellow
Reduced Computational Burden of Modulated Model-Predictive Control for Synchronous Reluctance Motor Drive Applications
Riccio, Jacopo; Karamanakos, Petros; Degano, Michele; Gerada, Chris; Zanchetta, Pericle
Authors
Petros Karamanakos
MICHELE DEGANO Michele.Degano@nottingham.ac.uk
Professor of Advanced Electrical Machines
CHRISTOPHER GERADA CHRIS.GERADA@NOTTINGHAM.AC.UK
Professor of Electrical Machines
PERICLE ZANCHETTA pericle.zanchetta@nottingham.ac.uk
Professor of Control Engineering
Abstract
This paper introduces a novel geometric approach to significantly reduce the computational burden of modulated predictive controllers while maintaining the same steady-state performance and satisfactory dynamic behavior. The proposed geometric method leverages the symmetric properties of the active vectors with respect to the zero vectors in two-level inverters. In addition, the structure of the controller is designed to include the integral of error terms, ensuring zero steady-state tracking error. Several operating points are considered and compared with respect to standard modulated model-predictive control approaches showing similar steady-state performance with a reduced computational effort (about 50%). This enables a broader spectrum of power electronic systems and applications that can be used with the same steady-state performance of standard modulated model-predictive control (M2PC). In addition, the latter option allows for the application of M2PC with high switching-frequency devices, given that a higher sampling frequency leads to an increased switching frequency. The effectiveness of the proposed approach is demonstrated with a synchronous reluctance motor drive application.
Citation
Riccio, J., Karamanakos, P., Degano, M., Gerada, C., & Zanchetta, P. (2023). Reduced Computational Burden of Modulated Model-Predictive Control for Synchronous Reluctance Motor Drive Applications. In 2023 IEEE Energy Conversion Congress and Exposition (ECCE) (4995-5002). https://doi.org/10.1109/ECCE53617.2023.10362110
Conference Name | 2023 IEEE Energy Conversion Congress and Exposition, ECCE 2023 |
---|---|
Conference Location | Nashville, TN. USA |
Start Date | Oct 29, 2023 |
End Date | Nov 2, 2023 |
Acceptance Date | Oct 29, 2023 |
Online Publication Date | Dec 29, 2023 |
Publication Date | Dec 29, 2023 |
Deposit Date | Apr 26, 2024 |
Publisher | Institute of Electrical and Electronics Engineers |
Pages | 4995-5002 |
Book Title | 2023 IEEE Energy Conversion Congress and Exposition (ECCE) |
ISBN | 979-8-3503-1643-8 |
DOI | https://doi.org/10.1109/ECCE53617.2023.10362110 |
Public URL | https://nottingham-repository.worktribe.com/output/29539803 |
Publisher URL | https://ieeexplore.ieee.org/document/10362110 |
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