Improved Incremental Model Based Deadbeat Model Predictive Current Control Method for Five-Phase PMSM

Due to low computational burden, high control bandwidth, excellent dynamic performance, and no overshoot, the deadbeat model predictive current control (DB-MPCC) method has great application potential in five-phase permanent magnet synchronous motors (F-PMSMs). However, the high dependence on the accuracy of the prediction model seriously affects the practical performance and application of the DB-MPCC method. This paper proposes an improved incremental model based DB-MPCC (IIM-DB-MPCC) method for F-PMSMs, which improves the discretization accuracy of the prediction model and effectively reduces the dependence of the DB-MPCC method on the accuracy of the prediction model. Firstly, the prediction model of the F-PMSM is discretized using the zero-order holding (ZOH) method, and the quantized control of the model discretization error is realized. Then, based on the improved discrete prediction model, the IIM-DB-MPCC method is proposed. Simulation test results show that, compared with the DB-MPCC method, the IIM-DB-MPCC method proposed in this paper can obtain higher prediction model accuracy than the Euler method at a low computational burden cost. Moreover, the static error caused by the mismatch of prediction model parameters is eliminated and the current and speed control accuracy of the F-PMSM is improved.