Publication:
Off-line robust constrained MPC for linear time-varying systems with persistent disturbances

dc.contributor.authorP. Bumroongsrien_US
dc.contributor.authorS. Kheawhomen_US
dc.contributor.otherMahidol Universityen_US
dc.contributor.otherChulalongkorn Universityen_US
dc.date.accessioned2018-11-09T02:17:14Z
dc.date.available2018-11-09T02:17:14Z
dc.date.issued2014-01-01en_US
dc.description.abstractAn off-line robust constrained model predictive control (MPC) algorithm for linear time-varying (LTV) systems is developed. A novel feature is the fact that both model uncertainty and bounded additive disturbance are explicitly taken into account in the off-line formulation of MPC. In order to reduce the on-line computational burdens, a sequence of explicit control laws corresponding to a sequence of positively invariant sets is computed off-line. At each sampling time, the smallest positively invariant set containing the measured state is determined and the corresponding control law is implemented in the process. The proposed MPC algorithm can guarantee robust stability while ensuring the satisfaction of input and output constraints. The effectiveness of the proposed MPC algorithm is illustrated by two examples. © 2014 P. Bumroongsri and S. Kheawhom.en_US
dc.identifier.citationMathematical Problems in Engineering. Vol.2014, (2014)en_US
dc.identifier.doi10.1155/2014/936093en_US
dc.identifier.issn15635147en_US
dc.identifier.issn1024123Xen_US
dc.identifier.other2-s2.0-84899930731en_US
dc.identifier.urihttps://repository.li.mahidol.ac.th/handle/20.500.14594/33893
dc.rightsMahidol Universityen_US
dc.rights.holderSCOPUSen_US
dc.source.urihttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84899930731&origin=inwarden_US
dc.subjectEngineeringen_US
dc.subjectMathematicsen_US
dc.titleOff-line robust constrained MPC for linear time-varying systems with persistent disturbancesen_US
dc.typeArticleen_US
dspace.entity.typePublication
mu.datasource.scopushttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84899930731&origin=inwarden_US

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