Finite control set model predictive control (FCS-MPC) stands out for fast dynamics and easy inclusion of multiple nonlinear control objectives. However, for long horizontal prediction or complex topologies with multiple levels and phases, the required computation burden surges exponentially as the increases of candidate switch states during one control period. This phenomenon leads to longer sample period to guarantee enough time for traverse progress of cost function minimization. In other words, the allowed highest switching frequency is bounded considerably far from the physical limits, especially for wide-band semiconductor applications. To overcome this issue, the parallel computing characteristic of artificial neural network (ANN) motivates the idea of an ANN-based FCS-MPC imitator (ANN-MPC). In this article, ANN-MPC is implemented on a neutral point clamped (NPC) converter using a shallow neural network. The expert (FCS-MPC) is initially designed, and the basic structure, including activation function selection, training data generation, and offline training progress, and online operation of the imitator (ANN-MPC) are then discussed. After the design of the expert and imitator, a comparative analysis is conducted by field programmable gate array (FPGA) in-the-loop implementation in MATLAB/Simulink environment. The verification results of ANN-MPC show highly similarly qualified control performance and considerably reduced computation resource requirement.

有限控制集模型预测控制 (FCS-MPC) 以快速动态和易于包含多个非线性控制目标而著称。然而，对于长水平预测或具有多层次和多相位的复杂拓扑，所需的计算负担随着一个控制周期内候选开关状态的增加而呈指数增长。这种现象导致更长的采样周期，以保证有足够的时间遍历成本函数最小化。换言之，允许的最高开关频率与物理极限相差甚远，尤其是对于宽带半导体应用。为了克服这个问题，人工神经网络 (ANN) 的并行计算特性激发了基于 ANN 的 FCS-MPC 模仿器 (ANN-MPC) 的想法。在本文中，ANN-MPC 是在使用浅层神经网络的中性点钳位 (NPC) 转换器上实现的。初步设计了专家（FCS-MPC），然后讨论了基本结构，包括激活函数选择、训练数据生成和离线训练进度，以及模仿者（ANN-MPC）的在线操作。在专家和模仿者的设计之后，通过现场可编程门阵列（FPGA）在MATLAB/Simulink环境下的在环实现进行了对比分析。ANN-MPC 的验证结果显示出高度相似的控制性能和显着降低的计算资源需求。然后讨论了离线训练进度，以及模仿者（ANN-MPC）的在线操作。在专家和模仿者的设计之后，通过现场可编程门阵列（FPGA）在MATLAB/Simulink环境下的在环实现进行了对比分析。ANN-MPC 的验证结果显示出高度相似的控制性能和显着降低的计算资源需求。然后讨论了离线训练进度，以及模仿者（ANN-MPC）的在线操作。在专家和模仿者的设计之后，通过现场可编程门阵列（FPGA）在MATLAB/Simulink环境下的在环实现进行了对比分析。ANN-MPC 的验证结果显示出高度相似的控制性能和显着降低的计算资源需求。