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Two-degree-of-freedom based robust iterative learning control for uncertain LTI systems

This paper proposes a robust iterative learning control based on a two-degree-of-freedom (2DOF) framework for linear-time-invariant (LTI) systems with uncertainties. It is motivated by the unavoidable model plant mismatches and the presence of the uncertainties in practical process, which hinder the application of the precise model-based iterative learning control (ILC) techniques in practice. In this note, by using the connection between the robust performance condition and the ILC convergence condition, we get a learning controller straightforward from the existing 2DOF control system. It's also shown that for a proper choice of the 2DOF controller and the robust weighting function satisfying the specified conditions, the designed ILC can get a perfect robust performance at the first iteration when the iterative learning control is not effective; in addition, the tracking error decreases along the iteration axis and finally convergences to a small neighborhood of zero.