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dc.contributor.advisorVincent, Tyrone
dc.contributor.advisorKee, R. J.
dc.contributor.authorSartipizadeh, Hossein
dc.date.accessioned2017-02-22T17:13:42Z
dc.date.accessioned2022-02-03T12:59:52Z
dc.date.available2017-02-22T17:13:42Z
dc.date.available2022-02-03T12:59:52Z
dc.date.issued2017
dc.identifierT 8215
dc.identifier.urihttps://hdl.handle.net/11124/170672
dc.descriptionIncludes bibliographical references.
dc.description2017 Spring.
dc.description.abstractModel Predictive Control (MPC) of processes when uncertainty is involved is the topic of this thesis. Specifically, a method to characterize parametric uncertainty for robust model predictive control is studied. The goal is to reduce the computational complexity of robust MPC and robust Moving Horizon Estimation (MHE). The main element of this method is the computation of an approximate convex hull that approximately covers the system uncertainty in a new output prediction mapping. Given the complete uncertainty set, an approximate convex hull is computed to determine an efficient set of extreme points to represent this set. The calculated set is a subset of the uncertainty set and can be significantly smaller than the original set, which results in decreasing the computational complexity. A measure of the approximation to the original set is used to provide robust output prediction that is guaranteed to hold for all systems in the original set. In other words, the introduced method provides a dynamic guaranteed bound on all possible output trajectories. The control performance of the proposed method will be investigated on the methane reforming process as a key element of fuel cells, and on a DC-DC floating interleaved boost converter.
dc.format.mediumborn digital
dc.format.mediumdoctoral dissertations
dc.languageEnglish
dc.language.isoeng
dc.publisherColorado School of Mines. Arthur Lakes Library
dc.relation.ispartof2017 - Mines Theses & Dissertations
dc.rightsCopyright of the original work is retained by the author.
dc.subjectmethane reforming
dc.subjectrobust model predictive control
dc.subjectapproximate convex hull
dc.subjectrobust moving horizon estimation
dc.subjectmodel uncertainty
dc.titleUncertainty characterization in robust MPC using an approximate convex hull
dc.typeText
dc.contributor.committeememberMoore, Kevin L., 1960-
dc.contributor.committeememberJohnson, Kathryn E.
dc.contributor.committeememberZhang, Xiaoli
thesis.degree.nameDoctor of Philosophy (Ph.D.)
thesis.degree.levelDoctoral
thesis.degree.disciplineElectrical Engineering and Computer Science
thesis.degree.grantorColorado School of Mines


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