Fish Growth Tracking and Mortality Monitoring: Control Design and Comparisons

Monitoring the water quality and controlling the feeding are essential functions in balancing fish productivity and shaping fish's life history in the fish growth process. Currently, most fish-feeding processes are conducted manually in different phases and are not optimized. The feeding technique influences fish growth through the feed conversion rate. In addition, the high concentration level of ammonia affects the water quality, resulting in fish survival and mass death. Therefore, there is a crucial need to develop control strategies to determine optimal, efficient, and reliable feeding processes and monitor water quality simultaneously. In this paper, we revisit the representative fish growth model describing the total biomass change by incorporating the fish population density and mortality. We specifically focus on relative feeding as a manipulated variable to design traditional and optimal control to track the desired weight reference within the suboptimal temperature and Dissolved Oxygen (DO) profiles under different levels of unionized ammonia (UIA) exposures. Then, we propose an optimal algorithm that optimizes the feeding and water quality of the dynamic fish population growth process. We also show that the model predictive control decreases fish mortality and reduces food consumption in all different cases by an average of 26.9% compared to the bang-bang controller, 22.6% compared to the PID controller.