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DoubleObjective Finite Control Set ModelFree
Predictive Control with DSVM for PMSM Drives
Beishi Zhao*, Hongmei Li¢Ó and Jingkui Mao* 
Abstract 
Discrete space vector modulation (DSVM) is an effective method to improve the steadystate performance of the finite control
set predictive control for permanent magnet synchronous motor drive systems. However, it requires complex computations due
to the presence of numerous virtual voltage vectors. This paper proposes an improved finite control set modelfree predictive
control using DSVM to reduce the computational burden. First, modelfree deadbeat current control is used to generate the
reference voltage vector. Then, based on the principle that the voltage vector closest to the reference voltage vector minimizes
the cost function, the optimal voltage vector is obtained in an effective way which avoids evaluation of the cost function.
Additionally, in order to implement doubleobjective control, a twolevel decisional cost function is designed to sequentially
reduce the stator currents tracking error and the inverter switching frequency. The effectiveness of the proposed control is
validated based on experimental tests. 
Keyword 
doubleobjective control, finite control set predictive control, low computation complexity, PMSM drive system 
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