  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.
