Super-Twisting Algorithm Sliding Mode Observer-Based Multi-Vector Model-Free Predictive Control Method for PMSM

This paper proposes a novel Super-twisting Algorithm Sliding Mode Observer-based Multi-vector Model-free Predictive Control (STASMO-MVMPC) method. The proposed method is applied to a permanent magnet synchronous motor (PMSM) drive system to enhance control performance. First, a novel STA-SMO is designed as part of the STASMO-MVMPC, offering faster convergence and more accurate estimation compared to the conventional ISMO. Furthermore, the STA-SMO is based on Lyapunov design, ensuring the stability of the system. Additionally, STASMO-MVMPC applies multi-vector control to improve the system's steady-state performance. Finally, the simulation results show that the proposed method is superior to the conventional methods in terms of dynamic convergence speed, total harmonic distortion (THD) and torque ripple.