An Iterative Learning Model Predictive Control Strategy for Evaporator

Superheat degree of evaporator in heating, ventilating, and air-conditioning (HVAC) system is a crucial variable, which not only maintains the system stabilty, but also impacts the system efficiency. In this study, an iterative learning model predictive control (ILMPC) strategy for the evaporator in vapor compressor refrigeration cycle (VCC) system is proposed. First of all, a simple model of evaporator has been presented by mechanism knowledge and system identification. Then, based on the iterative learning theory and model predictive control method, an ILMPC controller is proposed to control the superheat degree. Finally, a simulation is provided to test the effectiveness of the proposed control strategy.