System identification and control design of vapor compression cycle systems

In vapor compression cycle (VCC) systems, it is desirable to control the thermodynamic cycle effectively 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. This paper describes a novel two-stage control system design, in which the first stage considers the application of system identification techniques to obtain models using experimental data of a vapor compression plant. Several models were identified, wherein the output parameters within each model shared the same inputs and the one picked up for control design has the largest level of fitness. In the second stage, a set of improved control methods are implemented to design controllers for thermodynamic states of the VCC system based on the identified models. The methods include a new linear matrix inequality (LMI)-based guaranteed control, H_∞ controller, Kalman filter and the Linear Quadratic Gaussian Regulator (LQGR). The ensuing results of typical simulation on a lab-scale vapor compression plant have illustrated the effectiveness of the developed approach.