Symmetric Generalized Two-Vector Model Predictive Control With Optimal Vector Sequence and Duty Cycle for Induction Motor Drives

Abstract

Two-vector-based model predictive control (MPC) with optimal cycle has been well studied and widely applied in induction motor drives. The past study mainly focuses on the vector selection and duty cycle optimization. It is shown that the steady-state performance of MPC using two arbitrary voltage vectors during one control period is better than that using a fixed combination of a non-zero vector and a zero vector, especially at high speeds. However, the optimal pulse pattern is often ignored or difficult to be balanced in the state-of-the-art methods. This paper proposes an improved symmetric generalized two-vector-based MPC (SGTV-MPC) method to further reduce the current harmonics at steady state. At first, a non-zero vector is distributed equally on both sizes of the middle vector, which is not restricted to a zero vector but possibly a non-zero vector. An efficient way is employed to select the optimal vectors by comparing the sum of their respective duty cycle from deadbeat control based on space vector modulation. Then, not only the middle vector may be a non-zero vector, but also the first and last non-zero vectors are no longer equally distributed on both sizes of the middle vector. In fact, the duty cycle of the first vector is calculated by minimizing the current harmonics during one control period. Finally, the experimental results show that the proposed SGTV-MPC can reduce the current THD by 23.9% at most compared to the conventional generalized two-vector-based MPC, while maintaining similar switching frequency.