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    Model Predictive Control Paradigms for Fish Growth Reference Tracking in Precision Aquaculture

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    Type
    Preprint
    Authors
    Chahid, Abderrazak cc
    Ndoye, Ibrahima
    Majoris, John E. cc
    Berumen, Michael L. cc
    Laleg-Kirati, Taous-Meriem cc
    KAUST Department
    Electrical Engineering Program
    Physical Science and Engineering (PSE) Division
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Red Sea Research Center (RSRC)
    Marine Science Program
    Biological and Environmental Sciences and Engineering (BESE) Division
    Computational Bioscience Research Center (CBRC)
    Date
    2021-01-29
    Permanent link to this record
    http://hdl.handle.net/10754/667188
    
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    Abstract
    In 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 the feeding rate, temperature, and dissolved oxygen. This paper provides a comparative study of three model predictive control (MPC) strategies for fish growth reference tracking under a representative bioenergetic growth model in precision aquaculture. We propose to evaluate three 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 penalizes the feed conversion ratio (FCR), which is the ratio between food quantity and fish weight gain while minimizing the actual 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 the cost of food. Numerical simulations that integrate a realistic bioenergetic fish growth model of Nile tilapia (Oreochromis niloticus) are illustrated to examine the comparative performance of the three proposed optimal control strategies.
    Sponsors
    This 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
    Publisher
    arXiv
    arXiv
    2102.00004
    Additional Links
    https://arxiv.org/pdf/2102.00004
    Collections
    Biological and Environmental Sciences and Engineering (BESE) Division; Red Sea Research Center (RSRC); Preprints; Marine Science Program; Physical Science and Engineering (PSE) Division; Electrical Engineering Program; Computational Bioscience Research Center (CBRC); Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

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