Low Complexity Dual-Vector Model Predictive Current Control for Surface-Mounted Permanent Magnet Synchronous Motor Drives
In dual-vector model predictive current control (MPCC), an active voltage vector followed by either a zero or an active voltage vector is applied in one control period. Due to a high number of possible combinations of voltage vector pairs, determining the optimal pair becomes computationally expensive. A novel approach is proposed for reducing the number of candidate active voltage vectors. The concept is to use the projection of the current error vector onto the active voltage vector which minimizes the cost function. The proposed dual-vector MPCC is implemented in the rotor reference frame and does not need additional coordinate transformations and associated trigonometric calculations. Thus, it is simple to be digitally implemented. Furthermore, a novel method, which relies less on the system model, is proposed for determining the duty cycle of the active voltage vectors. Therefore, sensitivity to the erroneous assumption of motor parameters, particularly at low speeds, is alleviated. The performance of a permanent magnet synchronous motor drive under the proposed MPCC is compared with a most recently introduced dual-vector MPCC based on deadbeat control. Experimental results with the accurate and erroneous assumption of motor parameters verify the superiority of the proposed method in terms of computation simplicity, stator current and torque quality, and dynamic response.
Publication Source (Journal or Book title)
IEEE Journal of Emerging and Selected Topics in Power Electronics
Chen, J., Qin, Y., Bozorgi, A., & Farasat, M. (2020). Low Complexity Dual-Vector Model Predictive Current Control for Surface-Mounted Permanent Magnet Synchronous Motor Drives. IEEE Journal of Emerging and Selected Topics in Power Electronics, 8 (3), 2655-2663. https://doi.org/10.1109/JESTPE.2019.2917865