Explicit approaches to constrained model predictive control: A survey

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creativework.keywordsConstraints, Model predictive control, Multi-parametric quadratic programming, Piecewise linear controllers
creativework.publisherResearch Council of Norwayen
dc.contributor.authorGrancharova A.
dc.contributor.authorJohansen T.A.
dc.date.accessioned2024-07-10T14:27:03Z
dc.date.accessioned2024-07-10T14:46:57Z
dc.date.available2024-07-10T14:27:03Z
dc.date.available2024-07-10T14:46:57Z
dc.date.issued2004-01-01
dc.description.abstractThis paper presents a review of the explicit approaches to constrained model predictive control. The main motivation behind the explicit solution is that it avoids the need for real-time optimization, and thus allows implementation at high sampling frequencies in real-time systems with high reliability and low software complexity. The paper is organized as follows. Section 1 includes formulation of the constrained linear quadratic regulation (LQR) problem, summary of the implicit approaches, and the basics of the model predictive control (MPC). Sections 2 and 3 consider respectively the exact and the approximate approaches to explicit solution of constrained MPC problems, together with several examples.
dc.identifier.doi10.4173/mic.2004.3.1
dc.identifier.issn0332-7353
dc.identifier.scopusSCOPUS_ID:11144272593en
dc.identifier.urihttps://rlib.uctm.edu/handle/123456789/93
dc.language.isoen
dc.source.urihttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=11144272593&origin=inward
dc.titleExplicit approaches to constrained model predictive control: A survey
dc.typeReview
oaire.citation.issue3
oaire.citation.volume25
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