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arXiv · 2007.00430

On the Role of Models in Learning Control: Actor-Critic Iterative Learning Control

Maurice Poot·Jim Portegies·Tom Oomen
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Abstract

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.

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Maurice Poot, Jim Portegies, Tom Oomen. 2020-07-01. On the Role of Models in Learning Control: Actor-Critic Iterative Learning Control. https://arxiv.org/abs/2007.00430

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