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. The proposed approach 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 robust tracking of any desired target, and particularly is applicable for realizing the accurate tracking objective subject to the iteration-varying uncertainties with quasi-disappearing variations. Further, our developed results apply to iterative learning control, which is also verified by an example.

本文旨在处理数据驱动学习的控制分析和综合问题，不考虑未知的对象模型和迭代变化的不确定性。对于任何期望目标的跟踪，卡尔曼状态空间方法被提出来将其转化为两个鲁棒稳定性问题，从而在数据驱动控制和基于模型的控制之间架起了一座桥梁。所提出的方法可以在数据驱动学习的设计中采用扩展状态观察器 (ESO) 来克服迭代变化不确定性的影响。结果表明，基于ESO的数据驱动学习可确保无模型系统实现对任何目标的鲁棒跟踪，特别适用于实现具有准消失变化的迭代变化不确定性的精确跟踪目标。