Model Predictive Control of Permanent Magnet Synchronous Machine Based on Finite and Continuous Control Sets in Two Functional Quarters

In this paper, two schemes of model predictive control (MPC) method, named finite control set model predictive control (FCS-MPC) and dead-beat model predictive control (DB-MPC) as a continuous control set model predictive control (CCS-MPC) are applied and compared to control the current of a permanent magnet synchronous machine in energy recovery mode for the use of electric vehicles. The FCS-MPC strategy selects the optimal voltage vector and applies the control pulses directly to the inverter without using any modulators. In other side, DB-MPC is implemented through space vector pulse width modulation (SVPWM). The performance and results of both types of control strategies are extracted and compared using MATLAB Simulink software. The comparisons are made mainly in steady state and transient modes. Both control strategies are applied to a permanent magnet synchronous machine with the same parameters and with the same operating mode. The results show that the current steady state fluctuation is further reduced in the DB-MPC strategy and the transient state response is faster in the FCS-MPC strategy.