Control Analysis and Synthesis of Data-Driven Learning: A Kalman State-Space Approach

Abstract

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.

Submitted