  Iterative Learning Control (ILC) schemes can guarantee properties such as
asymptotic stability and monotonic error convergence, but do not, in general,
ensure adherence to output constraints. The topic of this paper is the design
of a reference-adapting ILC (RAILC) scheme, extending an existing ILC system
and capable of complying with output constraints. The underlying idea is to
scale the reference at every trial by using a conservative estimate of the
output's progression. Properties as the monotonic convergence above a threshold
and the respect of output constraints are formally proven. Numerical
simulations and experimental results reinforce our theoretical results.
