Please use this identifier to cite or link to this item: https://doi.org/10.1007/978-3-642-15621-2_14
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dc.titleBatch-to-batch iterative optimal control of batch processes based on dynamic quadratic criterion
dc.contributor.authorJia, L.
dc.contributor.authorShi, J.
dc.contributor.authorCheng, D.
dc.contributor.authorCao, L.
dc.contributor.authorChiu, M.-S.
dc.date.accessioned2014-06-19T06:13:03Z
dc.date.available2014-06-19T06:13:03Z
dc.date.issued2010
dc.identifier.citationJia, L., Shi, J., Cheng, D., Cao, L., Chiu, M.-S. (2010). Batch-to-batch iterative optimal control of batch processes based on dynamic quadratic criterion. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 6328 LNCS (PART 1) : 112-119. ScholarBank@NUS Repository. https://doi.org/10.1007/978-3-642-15621-2_14
dc.identifier.isbn3642156207
dc.identifier.issn03029743
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/74492
dc.description.abstractA novel dynamic parameters-based quadratic criterion-iterative learning control is proposed in this paper. Firstly, quadratic criterion-iterative learning control with dynamic parameters is used to improve the performance of iterative learning control. As a result, the proposed method can avoid the problem of initialization of the optimization controller parameters, which are usually resorted to trial and error procedure in the existing iterative algorithms used for the optimization of batch process. Next, we make the first attempt to give rigorous description and proof to verify that a perfect tracking performance can be obtained. Lastly, examples are used to illustrate the performance and applicability of the proposed method. © 2010 Springer-Verlag.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1007/978-3-642-15621-2_14
dc.sourceScopus
dc.subjectbatch processes
dc.subjectiterative learning control
dc.subjectquadratic criterion
dc.typeConference Paper
dc.contributor.departmentCHEMICAL & BIOMOLECULAR ENGINEERING
dc.description.doi10.1007/978-3-642-15621-2_14
dc.description.sourcetitleLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
dc.description.volume6328 LNCS
dc.description.issuePART 1
dc.description.page112-119
dc.identifier.isiut000286578800014
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