Applying of norm optimal iterative learning control on a multivariable test facility

Iterative learning control (ILC) is an approach suitable for systems which repeatedly perform a tracking task over a fixed time interval. However little attention has been paid to the case of multiple input, multiple output (MIMO) systems. In this paper theoretical results are derived and establish a close link between increased interaction, reduced robustness, slower convergence and greater control effort. Focusing on the popular class of norm optimal ILC (NOILC) algorithms, these findings are experimentally confirmed using a MIMO test facility which permits both exogenous disturbance injection and a variable level of coupling between input and output pairs. To address performance limitations 'point-to-point' NOILC is then introduced, in which the need to track the reference at all time points along the trial is relaxed.