电力变换器的一种简单且极其高效的预测控制方法
A Simple and Extremely Efficient Predictive Control for Power Converters
AI总结：
针对经典FCS-MPC计算负载高的问题，提出一种无需枚举的高效预测控制方法，经实验验证其控制性能与经典MPC相当，计算负担降低达88%。
AI中文摘要：
经典的基于有限控制集的模型预测控制（FCS-MPC）将优化问题简化为枚举搜索算法，该算法对电力变换器的控制简单且有效，但需要大量枚举操作，会增加计算负载和硬件成本。本研究提出一种新的简单预测控制技术，效率极高，该方法通过确定的可视化图直接选择最优矢量，仅需对代价函数进行重排和简单拟合律，无需任何枚举。在实验室构建的电力变换器及一组商用低成本数字控制器上进行验证，实验数据表明，该方法与经典MPC控制性能相同，计算负担显著降低（两电平变换器单步预测时降低达88%）。
英文摘要：
Classical finite control set based model predictive control (FCS-MPC) reduces the optimal problems to an enumerated searching algorithm, which is very simple and effective to control power converters. However, it requires a large amount of enumeration operations, increasing its computational load and hardware costs. In this work, we propose a new and simple predictive control technique with extreme efficiency. The proposal directly selects the optimal vector via determined visual maps, abstained solely requiring a rearrangement of the cost function and a simple fitting law, without any enumeration. It has been validated under a lab-constructed power converter and a set of commercialized low-cost digital controllers. Experimental data confirm that the proposal achieves the same control performance as classical MPC, with significant computational burden reduction (up to 88\% for one-step prediction of two-level converters)
