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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 9781618040046
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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