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MIMO model predictive control with local linear models

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dc.title MIMO model predictive control with local linear models en
dc.contributor.author Novák, Jakub
dc.contributor.author Chalupa, Petr
dc.contributor.author Bobál, Vladimír
dc.relation.ispartof Recent Researches in Automatic Control - 13th WSEAS International Conference on Automatic Control, Modelling and Simulation, ACMOS'11
dc.identifier.isbn 978-1-61804-004-6
dc.date.issued 2011
dc.citation.spage 189
dc.citation.epage 194
dc.event.title 13th WSEAS International Conference on Automatic Control, Modelling and Simulation, ACMOS'11
dc.event.location Lanzarote, Canary Islands
utb.event.state-en Spain
utb.event.state-cs Španělsko
dc.event.sdate 2011-05-27
dc.event.edate 2011-05-29
dc.type conferenceObject
dc.language.iso en
dc.relation.uri http://www.wseas.us/e-library/conferences/2011/Lanzarote/ACMOS/ACMOS-35.pdf
dc.subject Linearization en
dc.subject Local model networks en
dc.subject Multiple models en
dc.subject pH neutralization en
dc.subject Predictive control en
dc.description.abstract The performance of a Model Predictive Control (MPC) algorithm depends on the quality of the derived model. Using a divide-and-conquer strategy process operations were partitioned into several operating regions and within each region, a local linear model was developed to model the process. This set of locally linearized models was simply and effectively combined into a global description of a multivariable nonlinear plant. To save on computational load, a linear model was obtained by interpolating these linear models at each sample point and then this linearized model was used in a Generalized Predictive Control (GPC) framework to calculate the future behavior of the process. Thus, time-consuming nonlinear quadratic optimization calculations, which are normally necessary in nonlinear predictive control, can be avoided. Modeling and controller design procedure was demonstrated using a simulated pH neutralization process with two inputs and two outputs. en
utb.faculty Faculty of Applied Informatics
dc.identifier.uri http://hdl.handle.net/10563/1004762
utb.identifier.obdid 43865508
utb.identifier.scopus 2-s2.0-82555178549
utb.source d-scopus
dc.date.accessioned 2015-06-04T12:55:21Z
dc.date.available 2015-06-04T12:55:21Z
utb.contributor.internalauthor Novák, Jakub
utb.contributor.internalauthor Chalupa, Petr
utb.contributor.internalauthor Bobál, Vladimír
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