Efficient solution of nonlinear model predictive control by a restricted enumeration method

dc.creatorIsaza,Jhon
dc.creatorMuñoz,Diego A.
dc.creatorAlvarez,Hernan
dc.date2018-12-01
dc.date.accessioned2023-09-25T15:16:34Z
dc.date.available2023-09-25T15:16:34Z
dc.identifierhttp://scielo.senescyt.gob.ec/scielo.php?script=sci_arttext&pid=S1390-65422018000400013
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/8823999
dc.descriptionAbstract: This work presents an alternative method to solve the nonlinear program (NLP) for nonlinear model predictive control (NMPC) problems. The NLP is the most computational demanding task in NMPC, which limits the industrial implementation of this control strategy. Therefore, it is important to consider algorithms that can solve the nonlinear program, not only in real time but also guaranteeing feasibility. In this work, the restricted enumeration method is proposed as alternative to solve the NLP for NMPC problems, showing successful results for pH control in a sugar cane process plant. This method enumerates in restricted way a set of final control element possible positions around the current one. Next, it tests all positions in that set to find the best one, taken as the optimization solution.
dc.formattext/html
dc.languageen
dc.publisherUniversidad UTE
dc.relation10.29019/enfoqueute.v9n4.393
dc.rightsinfo:eu-repo/semantics/openAccess
dc.sourceEnfoque UTE v.9 n.4 2018
dc.subjectrestricted enumeration method
dc.subjectnonlinear program (NLP)
dc.subjectnonlinear model predictive control (NMPC)
dc.subjectpH control.
dc.titleEfficient solution of nonlinear model predictive control by a restricted enumeration method
dc.typeinfo:eu-repo/semantics/article


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