Convergence and robustness of a point-to-point iterative learning control algorithm

Iterative learning control (ILC) is a methodology applied to systems which repeatedly perform a tracking task defined over a fixed, finite time duration. In this approach the output is specified at all points in this interval, however there exists a broad class of applications in which the output is only important at a subset of time instants. An ILC update law is therefore derived which enables tracking at any subset of time points, with performance shown to increase as time points are removed from the tracking objective. Experimental results using a multi-variable test facility confirm that point-to-point ILC leads to superior performance than can be obtained using standard ILC and an a priori specified reference.