Enhanced Robust Model Predictive Control for Permanent Magnet Synchronous Motor Drives

Abstract:
Traditional Model Predictive Control (MPC) methods are typically designed for high switching frequency environments to achieve precise control. However, high switching frequencies significantly increase inverter switching losses, leading to substantial heat generation and power loss, which reduces overall control performance. At low switching frequencies, traditional MPC methods struggle to maintain system robustness and high performance. To address these issues, this paper presents an improved model predictive voltage control (MPVC) method designed to maintain excellent dynamic and steady-state performance and robustness in permanent magnet synchronous motors (PMSMs) operating at low switching frequencies. The proposed method first accurately models the PMSM mathematically, then uses current differences to obtain real-time equivalent inductance and flux-linkage data. Next, an improved cost function is constructed to determine the optimal voltage vector to be applied to the motor while reducing the system's switching frequency. Simulation results validate the effectiveness and feasibility of this method.