Control analysis and synthesis of data-driven learning for uncertain linear systems

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. (c) 2022 Elsevier Ltd. All rights reserved.