论文标题
在Loewner数据驱动的无限二维系统上
On Loewner data-driven control for infinite-dimensional systems
论文作者
论文摘要
在本文中，我们解决了Loewner数据驱动控制（L-DDC）方法的扩展。首先，通过合并两种称为Adaptive-Antoulas-Anderson（AAA）和向量拟合（VF）的替代近似方法来扩展这种方法。这些算法还包括最小二乘拟合，可提供额外的灵活性，并可以进行控制调整。其次，扩展了标准模型参考数据驱动的设置，以处理影响闭环目标函数数据和不确定性的噪声。这些提出的适应性产生了更强大的数据驱动控制设计。
In this paper, we address extensions of the Loewner Data-Driven Control (L-DDC) methodology. First, this approach is extended by incorporating two alternative approximation methods known as Adaptive-Antoulas-Anderson (AAA) and Vector Fitting (VF). These algorithms also include least squares fitting which provides additional flexibility and enables possible adjustments for control tuning. Secondly, the standard model reference data-driven setting is extended to handle noise affecting the data and uncertainty in the closed-loop objective function. These proposed adaptations yield a more robust data-driven control design.
