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dc.contributor.authorLong, C. E.en_US
dc.contributor.authorGatzke, Edward P.en_US
dc.contributor.authorVoit, Eberhard O.en_US
dc.date.accessioned2006-10-20T16:23:40Z
dc.date.available2006-10-20T16:23:40Z
dc.date.issued2003-06
dc.identifier.urihttp://hdl.handle.net/1853/12272
dc.descriptionPresented at the 2003 American Control Conference, June 4-6, 2003, Denver, Colorado, USAen
dc.description©2003 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or distribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.
dc.description.abstractThis paper presents a model predictive control (MPC) algorithm utilizing a state-space approach that allows for inferential control of unmeasured states using a prioritized control objective formulation. Knowledge of the unmeasured states is gained through the use of an external state estimation routine, while mixed-integer methods are used to implement the prioritization of the objectives. The capabilities of the algorithm are demonstrated by the application of the controller to a fermentation reactor model in a simulation environment.en
dc.format.extent603905 bytes
dc.format.mimetypeapplication/pdf
dc.language.isoen_USen
dc.publisherGeorgia Institute of Technologyen
dc.subjectBiotechnologyen
dc.subjectFermentationen
dc.subjectPredictive controlen
dc.subjectProcess controlen
dc.subjectState estimationen
dc.subjectState-space methodsen
dc.titleA mixed integer horizon formulation for prioritized objective inferential control of a bioprocess system.en
dc.typeArticleen
dc.publisher.originalInstitute of Electrical and Electronics Engineers, Inc., New York


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