Constrained generalized predictive control using relaxation with quadratic penalization

dc.creatorMuñoz Infante, Carlos Alberto
dc.creatorCipriano, Aldo
dc.date.accessioned2022-05-17T15:57:17Z
dc.date.available2022-05-17T15:57:17Z
dc.date.created2022-05-17T15:57:17Z
dc.date.issued2001
dc.identifier10.23919/ECC.2001.7076467
dc.identifier978-3952417362
dc.identifierhttps://doi.org/10.23919/ECC.2001.7076467
dc.identifierhttps://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=7076467
dc.identifierhttps://repositorio.uc.cl/handle/11534/64054
dc.description.abstractIn this paper an iterative algorithm will be derived to find a numerical solution for the generalized predictive control of linear systems with constraints on controlled and manipulated variables. The algorithm uses the relaxation method with quadratic penalization. Conditions are proposed for the convergence of the iterative solution and results of the application to a second order linear system are presented.
dc.languageen
dc.publisherIEEE
dc.relationEuropean Control Conference (2001 : Porto, Portugal)
dc.rightsacceso restringido
dc.subjectPredictive control
dc.subjectPrediction algorithms
dc.subjectAlgorithm design and analysis
dc.subjectVectors
dc.subjectLinear programming
dc.subjectEurope
dc.subjectConvergence
dc.titleConstrained generalized predictive control using relaxation with quadratic penalization
dc.typecomunicación de congreso


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