Learning for Advanced Motion Control

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Iterative Learning Control (ILC) can achieve perfect tracking performance for mechatronic systems. The aim of this paper is to present an ILC design tutorial for industrial mechatronic systems. First, a preliminary analysis reveals the potential performance improvement of ILC prior to its actual implementation. Second, a frequency domain approach is presented, where fast learning is achieved through noncausal model inversion, and safe and robust learning is achieved by employing a contraction mapping theorem in conjunction with nonparametric frequency response functions. The approach is demonstrated on a desktop printer. Finally, a detailed analysis of industrial motion systems leads to several shortcomings that obstruct the widespread implementation of ILC algorithms. An overview of recently developed algorithms, including extensions using machine learning algorithms, is outlined that are aimed to facilitate broad industrial deployment.
Original languageEnglish
Title of host publication2020 IEEE 16th International Workshop on Advanced Motion Control (AMC)
PublisherInstitute of Electrical and Electronics Engineers
ISBN (Electronic)978-1-7281-3189-4
Publication statusPublished - 10 Nov 2020
Event16th IEEE International Workshop on Advanced Motion Control, AMC 2020 - University of Agder, Campus Kristiansand, Kristiansand, Norway
Duration: 14 Sep 202016 Sep 2020
Conference number: 16


Conference16th IEEE International Workshop on Advanced Motion Control, AMC 2020
Abbreviated titleAMC 2020
Internet address


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