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

TL;DR

This paper compares three model predictive control strategies for optimizing fish growth in precision aquaculture, focusing on maximizing biomass while minimizing costs using a bioenergetic growth model.

Contribution

It introduces and evaluates three novel MPC formulations for fish growth reference tracking based on a bioenergetic model in aquaculture.

Findings

MPC strategies effectively track desired fish growth trajectories.

Tradeoffs between growth rate, energy use, and costs are demonstrated.

Numerical simulations validate the performance differences among the strategies.

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.