Dual-Vector Model Predictive Current Control of Permanent Magnet Synchronous Motor Drives With the Segment Golden Search Method - Junshuo Chen; Ying Zhang (IEEE Access, 2020)
Dual vector model predictive current control (DVMPCC) has been receiving plenty of concern due to its excellent static and dynamic performance. However, the application time of the selected voltage vector may be a wrong value because the calculation of duty cycle for each voltage vector is independent of the cost function. Here, to overcome the drawbacks of the current calculation methods, the paper introduces a novel approach to calculate the duty cycles for the two voltage vectors in one sampling time by combining the duty cycle calculation into the cost function. In this algorithm, the segment golden search method is used to find the optimal duty cycles for any pair of two voltage vectors. The cost function compares the available pairs of voltage vectors based on the obtained optimal duty cycles of each pair of voltage vectors to select the optimal pair of voltage vectors and duty cycles. In addition, to reduce the complexity of the algorithm, the redundant pairs of voltage vectors are eliminated and the available pairs of the voltage vectors is limit to five. Furthermore, a novel switching pattern is introduced to reduce the average switching frequency. When the inverter adopts the proposed MPCC, the optimal switching time and optimal voltage vector pair are both obtained at the same time, and the result duty cycle is always a reasonable number within the sampling interval. Comparative studies between the proposed method and a recently introduced duty cycle-based MPCC are carried out. The performance of a permanent magnet synchronous motor drive under the proposed MPCC confirms superiority in terms of average switching frequency, better quality currents, and electromagnetic torque ripple.