基于背靠背变换器的永磁同步电机简化模型预测控制

Jianqiao Zou
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引用次数: 0

摘要

为了确定最合适的切换向量,传统的模型预测控制(MPC)算法在每个采样周期进行8次搜索,时间负担极大。同时,传统的MPC方法也存在成本函数复杂等缺点,难以获得合理的良好性能。为了解决这一缺陷,本文提出了一种新的基于开关表的模型预测控制方法。首先,将MPC- st和MPC应用于基于背靠背变换器的永磁同步电机驱动系统。其次,在相同的条件下,通过综合仿真对MPC- st和MPC进行了详细的比较。充分证明了考虑其他性能参数的新型MPC-ST可以大大简化成本函数。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Simplified Model Predictive Control for Permanent Magnet Synchronous Machine based Back-to-Back Converter
To decide the most appropriate switching vector, traditional model predictive control (MPC) algorithms have to suffer extreme time burden for 8-time searching in each sampling period. Meanwhile, the traditional MPC methods have some other demerits, such as complex cost function to get reasonable good performance. In order to solve such defects, one new model predictive control method based switching table (MPC-ST) is proposed in this paper. Firstly, the MPC-ST and MPC are applied to permanent magnet synchronous machine (PMSM) drive system based back-to-back converter. Secondly, in the same conditions, a detail comparison between MPC-ST and MPC is conducted by comprehensive simulation. It fully demonstrates that the new MPC-ST considering other performance parameters can simplify cost function greatly.
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