论文标题
具有嘈杂数据的系统的强大数据驱动控制
Robust Data-Driven Control for Systems with Noisy Data
论文作者
论文摘要
本文基于嘈杂的输入输出数据提供了强大的数据驱动控制器设计，而没有关于噪音的统计属性的假设。我们从系统模型的直接数据代表开始，这些系统模型从行为系统理论中获取元素，然后分析“建模”误差的上界，并在数据表示的情况下使用噪声。根据衍生结构的结构方式，将一些预处理方法放入上下文中。最后，我们利用上限来开发乘坐数据噪声的强大控制器。
This paper presents a robust data-driven controller design based on the noisy input-output data without assumptions on the statistical properties of the noises. We start with the direct data-representation of system models that take elements from behavioral system theory, followed by analyses of the upper bound of the "modeling" error with the data representation with presence of noises. Some pre-conditioning methods are put into the context based on how the derived bound is structured. We lastly leverage the upper bound to develop robust controllers that ride through the data noises.
