Reduced Computational Burden of Modulated Model-Predictive Control for Synchronous Reluctance Motor Drive Applications

Riccio, Jacopo; Karamanakos, Petros; Degano, Michele; Gerada, Chris; Zanchetta, Pericle

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, October). Reduced Computational Burden of Modulated Model-Predictive Control for Synchronous Reluctance Motor Drive Applications. Presented at 2023 IEEE Energy Conversion Congress and Exposition, ECCE 2023, Nashville, TN. USA

• Presentation Conference Type: Edited Proceedings
• Conference Name: 2023 IEEE Energy Conversion Congress and Exposition, ECCE 2023
• 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
• Peer Reviewed: Peer Reviewed
• Pages: 4995-5002
• Series ISSN: 2329-3748
• 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