Model-based versus model-free feeding control and water-quality monitoring for fish-growth tracking in aquaculture systems

This paper proposes model-based and model-free control approaches to monitor the feeding rate and water quality for fish-growth tracking in aquaculture systems. The representative fish-growth model is revisited, which describes the total biomass change by incorporating the fish population density and mortality. Due to the challenging task of measuring the total fish biomass and population data, the new dynamic population model is validated with individual fish-growth data for tracking control. Ammonia exposure is a significant challenge in the fish-population growth tracking problem, affecting fish health and survival. To address this challenge, traditional and optimal controllers are first designed to track the weight reference within suboptimal temperature and dissolved oxygen (DO) profiles under various un-ionized ammonia (UIA) exposure levels by manipulating relative feeding. Then, a Q-learning approach is proposed to learn an optimal feeding-control policy from simulated data on fish-growth weight trajectories while managing ammonia effects. The proposed Q-learning feeding control prevents fish mortality and achieves good tracking errors for fish weight under UIA levels. However, it maintains a relative food consumption that potentially underfeeds fish. Finally, an optimal predictive algorithm that includes the temperature, DO, and UIA is proposed to optimize the feeding and water quality of the dynamic fish-population growth process, indicating that fish mortality is decreased and food consumption is reduced in all cases of UIA exposure.