  The high concentration level of the environmental factors, such as a high
ammonia concentration and pH level, affect the water quality, affecting fish's
survival and mass death. Therefore, there is a critical need to develop control
strategies to determine optimal, efficient, and reliable feeding and water
quality monitoring processes. In this paper, we revisit the representative fish
growth model describing the total biomass change by incorporating the fish
population density and mortality. Since the measurement data of the total
biomass and population from the aquaculture systems are limited and difficult
to obtain, we validate the new dynamic population model with the individual
fish growth data for tracking control purposes. We specifically focus on
relative feeding as a manipulated variable to design traditional and optimal
control to track the desired weight reference within the sub-optimal
temperature and dissolved oxygen profiles under different levels of unionized
ammonia exposure. Then, we propose a Q-learning approach that learns an optimal
feeding control policy from the simulated data of the fish growth weight
trajectories while managing the ammonia effects. The proposed Q-learning
feeding control prevents fish mortality and achieves good tracking errors of
the fish weight under the different levels of unionized ammonia. However, it
maintains a relative food consumption that potentially underfeeds the fish.
Finally, 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 of unionized ammonia exposure.
