Model predictive control (MPC) has been used widely in power electronics due to its simple concept, fast dynamic response, and good reference tracking. However, it suffers from parametric uncertainties, since it directly relies on the mathematical model of the system to predict the optimal switching states to be used at the next sampling time. As a result, uncertain parameters lead to an ill-designed MPC. Thus, this paper offers a model-free control strategy on the basis of artificial neural networks (ANNs), for mitigating the effects of parameter mismatching while having a little negative impact on the inverter's performance. This method includes two related stages. First, MPC is used as an expert to control the studied converter in order to provide the training data; while, in the second stage, the obtained dataset is utilized to train the proposed ANN which will be used directly to control the inverter without the requirement for the mathematical model of the system. The case study herein is based on a four-level three-cell flying capacitor inverter. In this study, MATLAB/Simulink is used to simulate the performance of the proposed control strategy, taking into account various operating conditions. Afterward, the simulation results are reported in comparison with the conventional MPC scheme, demonstrating the superior performance of the proposed control strategy in terms of getting low total harmonic distortion (THD) and the robustness against parameters mismatch, especially when changes occur in the system parameters.
翻译:由于其简单的概念、快速动态反应和良好的参考跟踪,模型预测控制(MPC)在电力电子中被广泛使用,由于其简单的概念、快速动态反应和良好的参考跟踪,模型预测控制(MPC)在权力电子中被广泛使用;然而,由于它直接依靠该系统的数学模型来预测下一个取样时使用的最佳切换状态,因此,不确定参数导致设计不当的 MPC。因此,本文件提供了一个基于人工神经神经网络(ANNS)的无模型控制战略,以减轻参数错配效应的影响,同时对逆向器的性能产生微小的负面影响。这种方法包括两个相关阶段。首先,MPC被用作专家来控制所研究的转换器,以便提供培训数据;在第二阶段,所获取的数据集被用来培训拟议的ANN(ANN),直接用于控制反向反转器,而无需使用系统的数学模型。本案例研究基于四层三电流变电容器。在本研究中,MATLAB/Simlink用于模拟所研究的高级转换器,以模拟所研究的高级变压战略的总体性,具体地将示范模式的运行情况纳入所报告常规控制战略。