永磁同步电机传统的模型预测控制(MPC)在一个控制周期内只作用一个电压矢量，需要较高的采样频率才能获得较好的控制性能。针对此问题，提出了一种基于拓展矢量集的模型预测控制算法，该算法无需改变采样频率即可实现电机控制性能和开关损耗的动态调节。由于拓展矢量集电压矢量多，为了降低计算量，提出了一种基于相邻矢量的电压矢量优化策略，将控制集候选电压矢量个数限制在11个以内。仿真和试验结果表明，所提基于拓展矢量集的模型预测控制算法不但保留了传统有限集MPC算法的动态性能，且可以在不改变采样频率下，通过调节拓展矢量集的电压矢量数量，实现控制性能和开关损耗的动态调节。

The traditional model predictive control（MPC）of permanent magnet synchronous motors（PMSM）only acts on one voltage vector in a control cycle, so a higher sampling frequency is needed to obtain better control performance. Aiming at the problem, a model predictive control algorithm based on extended vector set is proposed, which can realize the dynamic adjustment of motor control performance and switching loss without changing the sampling frequency. Because there are many voltage vectors in the extended vector set, in order to reduce the calculation, an optimization strategy of voltage vectors based on adjacent vectors is proposed, and the number of candidate voltage vectors in the control set is limited to 11. The simulation and experimental results show that the proposed PMSM model predictive control algorithm based on the extended vector set has the same dynamic performance to the traditional finite set model predictive control algorithm, and the control performance and switching loss can be adjusted by adjusting the number of voltage vectors in the extended vector set without changing the sampling frequency.

TM341

国家自然科学基金（2022YFB2403500）