Experimentally verified point-to-point iterative learning control for highly coupled systems

Abstract

Iterative learning control (ILC) is a well established approach for precision tracking control of systems which
perform a repeated tracking task defined over a fixed time interval. Despite a rich theoretical framework
accompanied by a wide array of application studies, comparatively little attention has been paid to the
case of multiple input, multiple output (MIMO) systems. Here the presence of interacting dynamics often
correlates with reduced performance. This article focuses on a general class of linear ILC algorithms and
establishes links between interaction dynamics and reduced robustness to modelling uncertainty, and slower
convergence. It then shows how these and other limitations can be addressed by relaxing the tracking
requirement to include only a subset of time points along the time duration. This is the first analysis to
show how so-called ‘point-to-point’ ILC can address performance limitations associated with highly coupled
systems. Theoretical observations are tested using a novel MIMO experimental test facility which permits
both exogenous disturbance injection and a variable level of coupling between input and output pairs. Results
compare experimental observations with theoretical predictions over a wide range of interaction levels and
with varying levels of injected noise.

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