Software-Based Automation and Control Architecture for a Magnetic Refrigeration System

In this study, software-based, measurement-driven automation and control architecture is developed for magnetic refrigeration systems. The proposed structure integrates real-time measurement data obtained from magnetic, hydraulic, and thermal sub-processes within a single decision layer. Control actions are generated based on cycle-level performance feedback. Instead of directly regulating absolute performance values, the control logic relies on performance trends between successive cycles as the primary decision variable. The method is experimentally implemented on a reciprocating magnetic refrigerator prototype. The system is first operated with fixed parameters, after which cycle-level adaptation is activated using measurement-based decisions. Experimental results show that adaptive control drives the system toward a stable and high-performance regime following a short transient phase. The average coefficient of performance (COP) increases from approximately 0.21 under manual operation to about 1.20 in adaptive operation, while cycle-to-cycle fluctuations are significantly reduced. The results indicate that operation based on fixed timing and preset parameters is insufficient for magnetic refrigeration systems. In contrast, software-based control using cyclic feedback shifts the system to a more stable and efficient regime. The proposed architecture provides a high-level control framework with low hardware dependency and can be adapted to different magnetic refrigeration configurations.