Model Predictive Current Control for Six-Phase PMSM with Steady-State Performance Improvement

Yongcan Huang, Senyi Liu (), Rui Pang, Xingbang Liu and Xi Rao
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Yongcan Huang: School of Energy and Environment, City University of Hong Kong, Hong Kong, China
Senyi Liu: Institute of Rail Transit, Tongji University, Shanghai 201804, China
Rui Pang: Institute of Rail Transit, Tongji University, Shanghai 201804, China
Xingbang Liu: Institute of Rail Transit, Tongji University, Shanghai 201804, China
Xi Rao: Institute of Rail Transit, Tongji University, Shanghai 201804, China

Energies, 2024, vol. 17, issue 21, 1-14

Abstract: The application of finite control set model predictive control (FCS-MPC) in six-phase permanent magnet synchronous motors (PMSMs) often faces a trade-off between computational burden and accurate voltage vector selection, as well as challenges related to harmonic components and torque generation. This paper introduces an improved model predictive current control (MPCC) method to address these problems. Firstly, 12 virtual voltage vectors are synthesized to improve torque output performance while suppressing harmonic currents. Then, to generate symmetrical switching signals and reduce switching loss, the largest basic vector used to synthesize the virtual vector is replaced by two medium vectors. Secondly, to solve the problem of the increased computational burden caused by the increase in discrete virtual vectors, a two-step vector selection method is proposed. In this method, each part is divided into several parts according to N , and the traditional cost function is also replaced by two-step functions. Different control performances can be achieved according to different values of N . Experimental results show that the proposed control scheme not only achieves stable current quality but also significantly improves steady-state performance throughout the entire speed range.

Keywords: MPC; six-phase machine; PMSM; performance improvement; voltage vector (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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