Enhanced Dual–Vector Model Predictive Control for PMSM Drives Using the Optimal Vector Selection Principle

Huang, Zhen; Wei, Qiang; Xiao, Xuechun; Xia, Yonghong; Rivera, Marco; Wheeler, Patrick

Abstract

The Dual–Vector model predictive control (DV–MPC) method can improve the steady–state control performance of motor drives compared to using the single–vector method in one switching cycle. However, this performance enhancement generally increases the computational burden due to the exponential increase in the number of vector selections, lowering the system’s dynamic response. Alternatively, limiting the vector combinations will sacrifice system steady–state performance. To address this issue, this paper proposes an enhanced DV–MPC method that can determine the optimal vector combinations along with their duration time within minimized calculation times. Compared to the existing DV–MPC methods, the proposed enhanced technique can achieve excellent steady–state performance while maintaining a low computational burden. These benefits have been demonstrated in the results from a 2.5k rpm permanent magnet synchronous motor drive.

Citation

Huang, Z., Wei, Q., Xiao, X., Xia, Y., Rivera, M., & Wheeler, P. (2023). Enhanced Dual–Vector Model Predictive Control for PMSM Drives Using the Optimal Vector Selection Principle. Energies, 16(22), Article 7482. https://doi.org/10.3390/en16227482

• Journal Article Type: Article
• Acceptance Date: Nov 6, 2023
• Online Publication Date: Nov 7, 2023
• Publication Date: Nov 7, 2023
• Deposit Date: Apr 12, 2024
• Publicly Available Date: Apr 12, 2024
• Journal: Energies
• Electronic ISSN: 1996-1073
• Publisher: MDPI
• Peer Reviewed: Peer Reviewed
• Volume: 16
• Issue: 22
• Article Number: 7482
• DOI: https://doi.org/10.3390/en16227482
• Keywords: Energy (miscellaneous); Energy Engineering and Power Technology; Renewable Energy, Sustainability and the Environment; Electrical and Electronic Engineering; Control and Optimization; Engineering (miscellaneous); Building and Construction
• Public URL: https://nottingham-repository.worktribe.com/output/27087246
• Publisher URL: https://www.mdpi.com/1996-1073/16/22/7482