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.
@article{arxiv.2007.00430,
title = {On the Role of Models in Learning Control: Actor-Critic Iterative Learning Control},
author = {Maurice Poot and Jim Portegies and Tom Oomen},
journal= {arXiv preprint arXiv:2007.00430},
year = {2020}
}