Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.engappai.2004.08.002
DC FieldValue
dc.titleAnalysis and comparison of iterative learning control schemes
dc.contributor.authorXu, J.-X.
dc.contributor.authorLee, T.H.
dc.contributor.authorZhang, H.-W.
dc.date.accessioned2014-06-17T02:38:41Z
dc.date.available2014-06-17T02:38:41Z
dc.date.issued2004-09
dc.identifier.citationXu, J.-X., Lee, T.H., Zhang, H.-W. (2004-09). Analysis and comparison of iterative learning control schemes. Engineering Applications of Artificial Intelligence 17 (6) : 675-686. ScholarBank@NUS Repository. https://doi.org/10.1016/j.engappai.2004.08.002
dc.identifier.issn09521976
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/55057
dc.description.abstractIterative learning control (ILC) schemes can be classified into the previous cycle learning (PCL), the current cycle learning (CCL) and the synergy - previous and current cycle learning (PCCL). In this work, we first present the configurations of various ILC schemes and the corresponding convergence conditions associated with each configuration. As a result of comparison, the PCCL scheme shows the ability of outperforming the PCL and CCL schemes owing to its underlying feature of two degrees of freedom design. Subsequently, we focus on two practical PCCL schemes with analysis and comparisons in frequency domain, substantiate the difference in the learning updating mechanisms, and in the sequel exploit the circumstances where one PCCL scheme can outperform the other. Based on system Bode plots, we can easily check the learning convergence condition, the complementary property of feedback and feedforward compensation, and which PCCL scheme can perform better. For the purpose of comparison and verification, both schemes are applied to a real-time ball-and-beam system. © 2004 Elsevier Ltd. All rights reserved.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1016/j.engappai.2004.08.002
dc.sourceScopus
dc.subjectExperimental verification
dc.subjectFeedback
dc.subjectFeedforward
dc.subjectIterative learning
dc.subjectProperty comparison
dc.typeArticle
dc.contributor.departmentELECTRICAL & COMPUTER ENGINEERING
dc.description.doi10.1016/j.engappai.2004.08.002
dc.description.sourcetitleEngineering Applications of Artificial Intelligence
dc.description.volume17
dc.description.issue6
dc.description.page675-686
dc.description.codenEAAIE
dc.identifier.isiut000224909500010
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