Finite control set model predictive control (FCS-MPC) has lately received noteworthy attention in the control of power converters. Such converters have a finite number of switching states, which ensure a small sample space of predictions and minimum computational burden. Although unanimously popular in most converters, multi-objective FCS-MPC (Mo-FCS-MPC) is a particularly attractive choice in the control of matrix converters (MCs), as it enables attainment of multiple objectives with relative ease. Conventionally, a weighing factor-based approach is undertaken in the implementation of Mo-FCS-MPC wherein a cost-function is framed such that each of its constituent objectives, is assigned a relative weight according to its significance. However, the tuning of weights is empirical in nature and hence tedious. This study proposes an improvised technique for implementing Mo-FCS-MPC in MCs while simultaneously meeting a number of objectives such as load current, source current, and input power factor control. Sector information from space vectors of reference output voltages and reference input currents, coupled with a fuzzy decision-making criterion is used to make the final switching decision, hence eliminating the conventional weighing factor-based approach. The inclusion of space vector modulation into predictive control enhances the quality of both loads as well as source current waveforms.

中文翻译：

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基于模糊逻辑和空间矢量的矩阵变换器改进多目标模型预测控制

有限控制集模型预测控制（FCS-MPC）最近在功率转换器的控制中受到了关注。这样的转换器具有有限数量的开关状态，这确保了较小的预测样本空间和最小的计算负担。尽管在大多数转换器中一致流行，但是多目标FCS-MPC（Mo-FCS-MPC）在矩阵转换器（MC）的控制中是一个特别吸引人的选择，因为它可以相对轻松地实现多个目标。常规地，在Mo-FCS-MPC的实施中采用基于权重因子的方法，其中对成本函数进行构架，使得根据其重要性为每个构成目标分配相对权重。但是，权重的调整本质上是经验性的，因此很繁琐。这项研究提出了一种在MC中实现Mo-FCS-MPC的简易技术，同时满足了许多目标，例如负载电流，源电流和输入功率因数控制。来自参考输出电压和参考输入电流的空间矢量的扇区信息，再加上模糊决策标准，用于做出最终的开关决策，从而消除了传统的基于权重因子的方法。将空间矢量调制纳入预测控制可提高负载和电源电流波形的质量。结合模糊决策标准来做出最终切换决策，从而消除了传统的基于权重因子的方法。将空间矢量调制纳入预测控制可提高负载和电源电流波形的质量。结合模糊决策标准来做出最终切换决策，从而消除了传统的基于权重因子的方法。将空间矢量调制纳入预测控制可提高负载和电源电流波形的质量。