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dc.contributor.authorChahid, Abderrazak
dc.contributor.authorNdoye, Ibrahima
dc.contributor.authorMajoris, John E.
dc.contributor.authorBerumen, Michael L.
dc.contributor.authorLaleg-Kirati, Taous-Meriem
dc.date.accessioned2021-08-25T11:42:52Z
dc.date.available2021-02-03T06:10:23Z
dc.date.available2021-08-25T11:42:52Z
dc.date.issued2021-08-12
dc.date.submitted2020-12-15
dc.identifier.citationChahid, A., N’Doye, I., Majoris, J. E., Berumen, M. L., & Laleg-Kirati, T. M. (2021). Model predictive control paradigms for fish growth reference tracking in precision aquaculture. Journal of Process Control, 105, 160–168. doi:10.1016/j.jprocont.2021.07.015
dc.identifier.issn0959-1524
dc.identifier.doi10.1016/j.jprocont.2021.07.015
dc.identifier.urihttp://hdl.handle.net/10754/667188
dc.description.abstractIn precision aquaculture, the primary goal is to maximize biomass production while minimizing production costs. This objective can be achieved by optimizing factors that have a strong influence on fish growth, such as feeding rate, temperature, and dissolved oxygen. This paper provides a comparative study of four model predictive control (MPC) strategies for fish growth reference tracking under a representative bioenergetic growth model in precision aquaculture. We propose to evaluate four candidate MPC formulations for fish growth reference tracking based on the receding horizon. The first MPC formulation tracks a desired fish growth trajectory while penalizing the feed ration, temperature, and dissolved oxygen. The second MPC optimization strategy directly optimizes the feed conversion ratio (FCR), which is the ratio between food quantity and fish weight gain at each sampling time. This FCR-like optimization strategy minimizes the feed while maximizing the predicted growth state's deviation from the given reference growth trajectory. The third MPC formulation includes a tradeoff between the growth rate trajectory tracking, the dynamic energy, and food cost. The last MPC formulation aims to maximize the fish growth rate while minimizing the costs. Numerical simulations that integrate a realistic bioenergetic fish growth model of Nile tilapia (Oreochromis niloticus) are illustrated to examine the comparative performance of the four proposed optimal control strategies. A sensitivity analysis is conducted to study the robustness of these MPC strategies with respect to the effect of the prediction horizon, the regularization term, and the additive input disturbances to the process. The obtained results show great potential and flexibility to meet the fish farmers’ needs depending on the type of fish, selling price, culture duration, and feed cost.
dc.description.sponsorshipThis work has been supported by the King Abdullah University of Science and Technology (KAUST), Base Research Fund (BAS/1/1627-01-01) to Taous Meriem Laleg and Base Research fund KAUST – AI Initiative Fund.
dc.publisherElsevier BV
dc.relation.urlhttps://linkinghub.elsevier.com/retrieve/pii/S0959152421001244
dc.rightsThis is an open access article under the CC BY-NC-ND license.
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.titleModel predictive control paradigms for fish growth reference tracking in precision aquaculture
dc.typeArticle
dc.contributor.departmentBiological and Environmental Science and Engineering (BESE) Division
dc.contributor.departmentComputational Bioscience Research Center (CBRC)
dc.contributor.departmentComputer, Electrical and Mathematical Science and Engineering (CEMSE) Division
dc.contributor.departmentElectrical and Computer Engineering Program
dc.contributor.departmentEstimation, Modeling and ANalysis Group
dc.contributor.departmentMarine Science Program
dc.contributor.departmentRed Sea Research Center (RSRC)
dc.contributor.departmentReef Ecology Lab
dc.identifier.journalJournal of Process Control
dc.eprint.versionPublisher's Version/PDF
dc.identifier.volume105
dc.identifier.pages160-168
dc.identifier.arxivid2102.00004
kaust.personChahid, Abderrazak
kaust.personNdoye, Ibrahima
kaust.personMajoris, John Edwin
kaust.personBerumen, Michael L.
kaust.personLaleg-Kirati, Taous-Meriem
kaust.grant.numberBAS/1/1627-01-01
dc.date.accepted2021-07-26
dc.identifier.eid2-s2.0-85112392080
refterms.dateFOA2021-02-03T06:43:10Z
kaust.acknowledged.supportUnitBAS
kaust.acknowledged.supportUnitBase Research Fund
dc.date.published-online2021-08-12
dc.date.published-print2021-09
dc.date.posted2021-01-29


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