Dimensionless Multi-Objective Model Predictive Current Control with Harmonic Subspace Suppression for Six-Phase PMSM Drives

Abstract

Multiphase permanent magnet synchronous motors (PMSMs) have been widely used in renewable energy, electric vehicle, and aviation applications. Model predictive control has emerged as a mainstream technique for multiphase drives, although conventional implementations suffer from the complexity of weighting factor tuning and excessive computational burden. This paper proposes a dimensionless multi-objective model predictive current control (MPCC) strategy for six-phase PMSM drives that operate without conventional weighting factors. Instead of penalizing harmonic subspace x-y currents through weighted cost terms, the proposed method pre-filters the 64 candidate voltage vectors to a selective set of 12 vectors whose α-β and x-y plane projections are inherently balanced, thereby constraining harmonic excitation at the source. A dimensionless multi-objective cost function is subsequently formulated, integrating current tracking, switching reduction, torque ripple suppression, and voltage/current constraints without additional weighting factors. The proposed strategy is evaluated under startup, steady-state, disturbance, and parameter sensitivity scenarios, with comparative analysis against conventional MPCC. Simulation results demonstrate that the phase current total harmonic distortion is reduced from 29.64% to 6.00%. The q-axis current settling time is reduced from 0.12 s to 0.025 s and the startup overshoot is reduced from 22 A to 11 A. The x-y subspace current ripple and average switching frequency are also reduced.