  Learning from data of past tasks can substantially improve the accuracy of
mechatronic systems. Often, for fast and safe learning a model of the system is
required. The aim of this paper is to develop a model-free approach for fast
and safe learning for mechatronic systems. The developed actor-critic iterative
learning control (ACILC) framework uses a feedforward parameterization with
basis functions. These basis functions encode implicit model knowledge and the
actor-critic algorithm learns the feedforward parameters without explicitly
using a model. Experimental results on a printer setup demonstrate that the
developed ACILC framework is capable of achieving the same feedforward signal
as preexisting model-based methods without using explicit model knowledge.
