This paper explores the controllability and interconnectedness of input-output relationships in vapor compression cycles. The magnitude of physical coupling between different outputs used in the feedback loop are examined. It is shown that an alternative to the conventional feedback configuration found in the literature has distinct benefits that allow for improved system regulation using simple classical control techniques. A relative gain array analysis technique is shown to be an effective method for identifying proper feedback configurations that maximize the controllability of vapor compression systems.

In vapor compression cycle systems, it is desirable to effectively control the thermodynamic cycle by controlling the thermodynamic states of the refrigerant. By controlling the thermodynamic states with an inner loop, supervisory algorithms can manage critical functions and objectives such as maintaining superheat and maximizing the coefficient of performance. In practice, it is generally preferred to tune multiple single-input-single-output (SISO) control inner loops rather than a single multiple-input-multiple-output control inner loop. This paper presents a process by which a simplified feedback control structure, amenable to a decoupled SISO control loop design, may be identified. In particular, the many possible candidate input-output (I/O) pairs for decentralized control are sorted via a decoupling metric, called the relative gain array number. From a reduced set of promising candidate I/O pairs, engineering insight is applied to arrive at the most effective pairings successfully verified on an experimental air-conditioning-and-refrigeration test stand.

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This paper presents a parameter sensitivity analysis for a low-order control-oriented dynamic model of a subcritical vapor compression system. The results are used to tune immeasurable model parameters, account for the unmodeled system dynamics, and are applied to fault detection residual design. The models are validated against data taken from an experimental test stand, and the sensitivity based model tuning is shown to improve the model accuracy while providing enhanced physical insight into system dynamics.

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