The spatial variation in water quality might adversely affect fish productivity, mortality, and sustainability of floating net cage aquaculture. Traditional floating net cages are often fixed and rely on manual environmental monitoring, which restricts their responsiveness to varying water conditions. To solve this problem, this study developed and tested a self-propelled floating net cage that can automatically move in real time in response to water quality conditions. The suggested system includes water quality monitoring, waypoint selection using Particle Swarm Optimization (PSO), fuzzy logic-based navigation, and cooperative formation control with autonomous sensor buoys. The system constantly checks environmental conditions to find proper relocation routes to locations of better water quality. Three fuzzy controller structures with 3, 5, and 7 membership functions were constructed and evaluated by field trials in aquaculture ponds. The results indicate that real-time water quality monitoring, PSO-based waypoint optimization, fuzzy navigation control, and cooperative formation maintenance may be successfully integrated into a single autonomous framework. The system identified the best spot out of four monitored regions and navigated to it based on the observed water quality. Among the investigated controllers, the configuration with seven membership functions showed the highest performance, lowering the average positioning error from 3.79 m to 1.25 m, while delivering smoother trajectories and more stable formation control. The results demonstrate that adaptive autonomous repositioning is technically feasible for floating net cage aquaculture under the field conditions evaluated. Still a proof-of-concept and needing long-term validation and broader performance comparisons, the study shows the potential of integrating real-time environmental monitoring, optimization-based navigation, fuzzy decision-making, and cooperative autonomous control to enable more adaptive and intelligent aquaculture management.