Optimization-based Tuning of Nonlinear Model Predictive Control with State Estimation

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dc.contributor.authorAli, Emaden_US
dc.contributor.authorZafiriou, E.en_US
dc.contributor.departmentISRen_US
dc.date.accessioned2007-05-23T09:54:07Z
dc.date.available2007-05-23T09:54:07Z
dc.date.issued1993en_US
dc.description.abstractNonlinear Model Predictive Controllers determine appropriate control actions by solving an on-line optimization problem. A nonlinear process model is utilized for on-line prediction, making such algorithms particularly appropriate for the control of chemical reactors. The algorithm presented in this paper incorporates an Extended Kalman Filter, which allows operations around unstable steady-state points. The paper proposes a formalization of the procedure for tuning the several parameters of the control algorithm. This is accomplished by specifying time-domain performance criteria and using an interactive multi- objective optimization package off-line to determine parameter values that satisfy these criteria. Three reactor examples are used to demonstrate the effectiveness of the proposed on-line algorithm and off-line procedure.en_US
dc.format.extent1194478 bytes
dc.format.mimetypeapplication/pdf
dc.identifier.urihttp://hdl.handle.net/1903/5396
dc.language.isoen_USen_US
dc.relation.ispartofseriesISR; TR 1993-51en_US
dc.subjectchemical process controlen_US
dc.subjectoptimizationen_US
dc.subjectpredictive controlen_US
dc.subjectestimationen_US
dc.subjectnonlinear systemsen_US
dc.subjectoptimal controlen_US
dc.subjectoptimizationen_US
dc.subjectChemical Process Systemsen_US
dc.titleOptimization-based Tuning of Nonlinear Model Predictive Control with State Estimationen_US
dc.typeTechnical Reporten_US

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