论文标题
数据驱动学习的控制分析和综合：卡尔曼州空间方法
Control Analysis and Synthesis of Data-Driven Learning: A Kalman State-Space Approach
论文作者
论文摘要
本文旨在处理数据驱动的学习的控制分析和综合问题，无论未知植物模型和与迭代变化的不确定性如何。为了跟踪任何所需的目标，提出了Kalman State空间方法，以将其转换为两个强大的稳定性问题，该问题桥接了数据驱动的控制和基于模型的控制之间的连接。这种方法还可以使扩展的州观察者（ESO）在数据驱动的学习设计中克服与迭代相反的不确定性的效果。结果表明，基于ESO的数据驱动的学习可确保无模型系统以实现任何所需目标的跟踪。特别是，我们的结果适用于迭代学习控制，这是通过示例验证的。
This paper aims to deal with the control analysis and synthesis problem of data-driven learning, regardless of unknown plant models and iteration-varying uncertainties. For the tracking of any desired target, a Kalman state-space approach is presented to transform it into two robust stability problems, which bridges a connection between data-driven control and model-based control. This approach also makes it possible to employ the extended state observer (ESO) in the design of data-driven learning to overcome the effect of iteration-varying uncertainties. It is shown that ESO-based data-driven learning ensures model-free systems to achieve the tracking of any desired target. In particular, our results apply to iterative learning control, which is verified by an example.
