Sparse iterative learning control (SPILC): when to sample for resource-efficiency?

Tom Oomen, Cristian R. Rojas

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

Abstract

Iterative learning control enables the determination of optimal command inputs by learning from measured data of previous tasks. The aim of this paper is to address the negative impact of trial-varying disturbances that contaminate these measurements, both in terms of resource-efficient implementations and performance degradation. The proposed method is an optimal framework for ILC that enforces sparsity and related structure on the command signal. This is achieved through a convex relaxation relying on ℓ1 regularization. The approach is demonstrated on a benchmark motion system, confirming substantial extensions compared to earlier results.

Original languageEnglish
Title of host publicationProceedings - 2018 IEEE 15th International Workshop on Advanced Motion Control, AMC 2018
Place of PublicationPiscataway
PublisherInstitute of Electrical and Electronics Engineers
Pages497-502
Number of pages6
ISBN (Electronic)9781538619469
DOIs
Publication statusPublished - 1 Jun 2018
Event15th International Workshop on Advanced Motion Control (AMC 2018) - Shibaura Institute of Technology, Tokyo, Japan
Duration: 9 Mar 201811 Mar 2018
Conference number: 15
http://ewh.ieee.org/conf/amc/2018/

Conference

Conference15th International Workshop on Advanced Motion Control (AMC 2018)
Abbreviated titleAMC 2018
Country/TerritoryJapan
CityTokyo
Period9/03/1811/03/18
OtherAMC2018 is the 15th in a series of biennial workshops that brings together researchers active in the field of advanced motion control to discuss current developments and future perspectives on motion control technology and applications. The workshop will be held at Shibaura Institute of Technology, Tokyo, Japan, during March 9-11, 2018.
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