The double vector model predictive control strategy can improve the steady-state control performance of the motor without significantly increasing switching losses. However

its vector determination method and duty cycle calculation process are more complex

which requires heavy computation burden. Therefore

a double-vector model predictive control scheme with low computational burden for PMSM was proposed. Firstly

when the PMSM operates in the steady-state

the candidate range of the first optimal active vector was reduced to three

including the first optimal vector adopted in the previous control period and its adjacent active voltage vectors. The cost function was sequentially substituted to determine the first optimal vector

thereby the number of comparisons decreases from six times to three times. Then

the remaining two vectors and the zero vector was considered as the alternatives for the second optimal vector

according to the deadbeat condition of the q-axis current

the duty cycle of all vector combinations was obtained separately. The vector combination and duty cycle that minimizes the cost function was obtained by substituting the vectors with their duty cycle into the cost function. Finally

the simulation models and experimental platforms were established

the stability

feasibility and effectiveness of the proposed model predictive control scheme with low computation burden were certified

respectively. The proposed model predictive control can achieve a current THD of 6.57% and a torque ripple of ±0.4 N·m with an average calculation time of only 15.3 μs. Compared with other model predictive control methods

it can obtain the best steady-state performance.