Low-Complexity Multi-Vector-Based Model Predictive Torque Control for PMSM with Voltage Pre-selection

In the double-vector-based model predictive torque control (MPTC), two voltage vectors are applied in one control period. Due to a large number of possible combinations among voltage vectors, the determination of optimal voltage vector pair is often complicated. This paper proposes a new approach to reduce the number of candidate active voltage vectors. The concept is to pre-select the active voltage vectors according to the stator flux vector error. The proposed MPTC is implemented in a stationary frame and avoids the complicated coordinate transformation as well as the tangent inverse calculations. Furthermore, a promising approach is conceived for calculating the duty cycle of active voltage vector, which can contribute to the less dependence on the system model, thus alleviating the sensitivity to motor parameter variations. To further improve the steady-state performance, a modified three-vector-based MPTC is newly put forward. Meanwhile, the proposed duty cycle calculation method is used to achieve the deadbeat control of torque and stator flux. Theoretical analyses and experimental results are given to verify the effectiveness of proposed MPTC methods.