  In Iterative Learning Control (ILC), a sequence of feedforward control
actions is generated at each iteration on the basis of partial model knowledge
and past measurements with the goal of steering the system toward a desired
reference trajectory. This is framed here as an online learning task, where the
decision-maker takes sequential decisions by solving a sequence of optimization
problems having only partial knowledge of the cost functions. Having
established this connection, the performance of an online gradient-descent
based scheme using inexact gradient information is analyzed in the setting of
dynamic and static regret, standard measures in online learning. Fundamental
limitations of the scheme and its integration with adaptation mechanisms are
further investigated, followed by numerical simulations on a benchmark ILC
problem.
