Finite-control-set multistep model predictive control based on incremental prediction model for SPMSM drivers

Dongwen Wang; Guangyao Lin; Wei Zhou; Mengfei Zhang

doi:10.1117/12.3037668

27 September 2024 Finite-control-set multistep model predictive control based on incremental prediction model for SPMSM drivers

Dongwen Wang, Guangyao Lin, Wei Zhou, Mengfei Zhang

Author Affiliations +

Proceedings Volume 13275, Sixth International Conference on Information Science, Electrical, and Automation Engineering (ISEAE 2024); 132753G (2024) https://doi.org/10.1117/12.3037668
Event: 6th International Conference on Information Science, Electrical, and Automation Engineering (ISEAE 2024), 2024, Wuhan, China

Abstract

Multi-step model predictive control (MMPC) for motor drives can reduce the switching loss, retard temperature rise, and improve the reliability of the control system. However, MMPC has a high computational complexity, which requires advanced microprocessors and raises the cost. In this paper, an optimized deadbeat voltage vector strategy based on receding horizon control is proposed. The MMPC model for PMSM is established, and then the proposed strategy transforms the multi-step model predictive voltage control (MPVC) into one-step MPVC, which simplifies the calculation process. Furthermore, the prediction horizon is paid attention to, and its contribution is analysed. Simulation results verify the effectiveness of the proposed method.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Dongwen Wang, Guangyao Lin, Wei Zhou, and Mengfei Zhang "Finite-control-set multistep model predictive control based on incremental prediction model for SPMSM drivers", Proc. SPIE 13275, Sixth International Conference on Information Science, Electrical, and Automation Engineering (ISEAE 2024), 132753G (27 September 2024); https://doi.org/10.1117/12.3037668

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