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Please use this identifier to cite or link to this item: https://doi.org/10.1016/S0952-1976(02)00008-8
DC FieldValue
dc.titleEnhancing trajectory tracking for a class of process control problems using iterative learning
dc.contributor.authorXu, J.-X.
dc.contributor.authorLee, T.-H.
dc.contributor.authorTan, Y.
dc.date.accessioned2014-06-17T02:48:23Z
dc.date.available2014-06-17T02:48:23Z
dc.date.issued2002-02
dc.identifier.citationXu, J.-X., Lee, T.-H., Tan, Y. (2002-02). Enhancing trajectory tracking for a class of process control problems using iterative learning. Engineering Applications of Artificial Intelligence 15 (1) : 53-64. ScholarBank@NUS Repository. https://doi.org/10.1016/S0952-1976(02)00008-8
dc.identifier.issn09521976
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/55898
dc.description.abstractA method of enhancing tracking in repetitive processes, which can be approximated by a first-order plus dead-time model is presented. Enhancement is achieved through filter-based iterative learning control (ILC). The design of the ILC parameters is conducted in frequency domain, which guarantees the convergence property in iteration domain. The filter-based ILC can be easily added to existing control systems. To clearly demonstrate the features of the proposed ILC, a water heating process under a PI controller is used as a testbed. The empirical results show improved tracking performance with iterative learning. © 2002 Elsevier Science Ltd. All rights reserved.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1016/S0952-1976(02)00008-8
dc.sourceScopus
dc.subjectEnhance tracking
dc.subjectFilter-based iterative learning control
dc.subjectFrequency convergence analysis
dc.typeArticle
dc.contributor.departmentELECTRICAL & COMPUTER ENGINEERING
dc.description.doi10.1016/S0952-1976(02)00008-8
dc.description.sourcetitleEngineering Applications of Artificial Intelligence
dc.description.volume15
dc.description.issue1
dc.description.page53-64
dc.description.codenEAAIE
dc.identifier.isiut000177546600006
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