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dc.contributor.authorOwa, K
dc.contributor.authorsharma, sanjay
dc.contributor.authorSutton, R
dc.date.accessioned2015-10-15T11:16:33Z
dc.date.available2015-10-15T11:16:33Z
dc.date.issued2014-07
dc.identifier.issn0959-6518
dc.identifier.issn2041-3041
dc.identifier.urihttp://hdl.handle.net/10026.1/3646
dc.description.abstract

<jats:p>This article presents the design, simulation and real-time implementation of a constrained non-linear model predictive controller for a coupled tank system. A novel wavelet-based function neural network model and a genetic algorithm online non-linear real-time optimisation approach were used in the non-linear model predictive controller strategy. A coupled tank system, which resembles operations in many chemical processes, is complex and has inherent non-linearity, and hence, controlling such system is a challenging task. Particularly important is low-level control where often instability and oscillatory responses are observed. This article designs a wavelet neural network with high predicting precision and time–frequency localisation characteristics for an online prediction model in the non-linear model predictive controller to show the effectiveness of this approach in controlling the liquid at low level. To speed up the training process, a fast global search stochastic non-linear conjugate wavelet gradient algorithm is initially used to train the wavelet neural network structure before the genetic algorithm optimisation technique is utilised to tune adaptively the wavelet neural network parameters. The non-linear model predictive controller algorithm is tested for both approaches: first, in a simulation using identified models, and second, in a real-time practical application to a single-input single-output system coupled tank system. The results show an excellent control performance with respect to mean square error and average control energy values obtained.</jats:p>

dc.format.extent419-432
dc.languageen
dc.language.isoen
dc.publisherSAGE Publications
dc.subjectWavelet neural network
dc.subjectartificial neural network
dc.subjectmodelling
dc.subjectsystem identification
dc.subjectnon-linear model predictive control
dc.subjectreal-time practical implementation
dc.subjectgenetic algorithms
dc.subjectsingle input single output
dc.subjectnon-linear optimisation
dc.subjectcoupled tank system
dc.subjectSimulink model
dc.titleA novel real-time non-linear wavelet-based model predictive controller for a coupled tank system
dc.typejournal-article
dc.typeJournal Article
plymouth.author-urlhttps://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000338012300007&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=11bb513d99f797142bcfeffcc58ea008
plymouth.issue6
plymouth.volume228
plymouth.publication-statusPublished
plymouth.journalProceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering
dc.identifier.doi10.1177/0959651813520149
plymouth.organisational-group/Plymouth
plymouth.organisational-group/Plymouth/Faculty of Science and Engineering
plymouth.organisational-group/Plymouth/REF 2021 Researchers by UoA
plymouth.organisational-group/Plymouth/REF 2021 Researchers by UoA/UoA12 Engineering
plymouth.organisational-group/Plymouth/Research Groups
plymouth.organisational-group/Plymouth/Research Groups/Marine Institute
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dc.identifier.eissn2041-3041
dc.rights.embargoperiodNot known
rioxxterms.versionofrecord10.1177/0959651813520149
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserved
rioxxterms.typeJournal Article/Review


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