论文标题
支持数据的预测控制的二次正则化：对电源转换器实验的理论和应用
Quadratic Regularization of Data-Enabled Predictive Control: Theory and Application to Power Converter Experiments
论文作者
论文摘要
通过直接从系统数据提供最佳控制输入来规避系统识别过程的数据驱动控制近年来引起了重新关注。在本文中，我们专注于理解正则化对支持数据的预测控制（DEEPC）算法的影响。我们提供理论动机和解释，以包括二次正则化项。我们的分析表明，二次正则化项导致有关影响数据的干扰的鲁棒和最佳解决方案。此外，当输入/输出约束不活跃时，二次正则化会导致DEEPC算法的封闭形式解决方案，从而实现快速计算。在此基础上，我们为数据驱动的同步和功率转换器的功率法规提出了一个框架，该框架通过高保真模拟和实验进行了测试。
Data-driven control that circumvents the process of system identification by providing optimal control inputs directly from system data has attracted renewed attention in recent years. In this paper, we focus on understanding the effects of the regularization on the data-enabled predictive control (DeePC) algorithm. We provide theoretical motivation and interpretation for including a quadratic regularization term. Our analysis shows that the quadratic regularization term leads to robust and optimal solutions with regards to disturbances affecting the data. Moreover, when the input/output constraints are inactive, the quadratic regularization leads to a closed-form solution of the DeePC algorithm and thus enables fast calculations. On this basis, we propose a framework for data-driven synchronization and power regulations of power converters, which is tested by high-fidelity simulations and experiments.
