Robust iterative learning control with current feedback for uncertain linear systems

Considering an uncertain plant in iterative learning control (ILC), robust convergence and robust stability are important issues. Since the feedback controller robustly stabilises the uncertain plant and has an effect on the convergence, if plays as significant a role as the learning controller does in the ILC system. To deal with both convergence and stability in ILC, we take account of an ILC scheme with current feedback in this paper. First, a few terms related to robust convergence are defined and a sufficient condition for robust convergence and robust stability free from uncertainty is obtained via structured singular value (mu) and linear fractional transformations (LFTs). Secondly, a synthesis method is presented on the basis of the proposed condition and D-K iteration. In this method, a feedback controller and learning controllers can be designed at one time and a weighting function is introduced to increase the learning performance. Lastly, through a computational experiment, we confirm the feasibility of the proposed method.