This letter proposes a data-driven iterative learning predictive control architecture for power converters. The main objectives of this letter are to enhance the robustness and remain the high performance of finite control-set model predictive control (FCS-MPC) under unmodeled dynamics and parameter mismatch conditions. More specifically, an iterative dynamic linearization technique is utilized to equivalently reformulate the nonlinear power converter system at each operating point. Based on this, a model-free adaptive control scheme is presented to iteratively determine the optimal control actions. Due to the incorporation of iterative learning control and data-driven concept into the FCS-MPC framework, the effect of parameter perturbations can be alleviated in the proposed method, while creating a positive effect on the tracking error. Finally, a convergence analysis is provided and experimental investigations on a three-level neutral-point-clamped (NPC) converter confirm the effectiveness of the proposed method.

中文翻译：

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电源转换器的数据驱动迭代学习预测控制


这封信提出了一种用于电源转换器的数据驱动的迭代学习预测控制架构。这封信的主要目标是增强鲁棒性并保持有限控制集模型预测控制（FCS-MPC）在未建模动态和参数失配条件下的高性能。更具体地，利用迭代动态线性化技术在每个工作点等效地重构非线性功率转换器系统。在此基础上，提出了一种无模型自适应控制方案来迭代确定最优控制动作。由于将迭代学习控制和数据驱动概念纳入FCS-MPC框架中，该方法可以减轻参数扰动的影响，同时对跟踪误差产生积极的影响。最后，提供了收敛性分析，并对三电平中性点钳位（NPC）转换器进行了实验研究，证实了该方法的有效性。