Model Predictive Control Algorithm of Dual Three-Phase Motor Considering Global Single Vector

In this article, a single vector model predictive control algorithm is proposed, which has low computation and can include all vectors of dual-three phase permanent magnet synchronous motor (DTP-PMSM) into optimization. In order to reduce the amount of calculation and increase the utilization ratio of bus voltage, the Single vector model predictive control based on maximum vector (MSVMPC) of DTP-PMSM only takes the outermost maximum vector into the optimization. In some cases of light load and low speed, because the vector length is not adjustable, the optional vector often does not match the actual voltage vector demand, resulting in torque fluctuations. Regarding the above problems, this article proposes a global single vector model predictive control algorithm (GSVMPC), which splits the predicted current change value at the next time caused by all voltage vectors of the dual three-phase motor into a combination of a group of basic voltage vector action results. Thus, the original need for global vector optimization is converted into the need to optimize only the basic voltage vector, and all voltage vectors are included in the optimization range of model prediction while achieving less computation. Finally, simulated results are given, results show that under the low switching frequency, low speed and light load operating conditions, compared with the MSVMPC, the torque fluctuation of GSVMPC can be significantly reduced.