Vapor compression refrigeration cycle(VCC) system is a high dimensional coupling thermodynamic system for which the controller design is a great challenge. In this paper, a model predictive control based energy effici...Vapor compression refrigeration cycle(VCC) system is a high dimensional coupling thermodynamic system for which the controller design is a great challenge. In this paper, a model predictive control based energy efficient control strategy which aims at maximizing the system efficiency is proposed. Firstly,according to the mass and energy conservation law, an analysis on the nonlinear relationship between superheat and cooling load is carried out, which can produce the maximal effect on the system performance. Then a model predictive control(MPC)based controller is developed for tracking the calculated setting curve of superheat degree and pressure difference based on model identified from data which can be obtained from an experimental rig. The proposed control strategy maximizes the coefficient of performance(COP) which depends on operating conditions, in the meantime, it meets the changing demands of cooling capacity.The effectiveness of the control performance is validated on the experimental rig.展开更多
基金supported by the National Natural Science Foundation of China(61233004,61221003,61374109,61473184,61703223,61703238)the National Basic Research Program of China(973 Program)(2013CB035500)+1 种基金Shandong Provincial Natural Science Foundation of China(ZR2017BF014,ZR2017MF017)the National Research Foundation of Singapore(NRF-2011,NRF-CRP001-090)
文摘Vapor compression refrigeration cycle(VCC) system is a high dimensional coupling thermodynamic system for which the controller design is a great challenge. In this paper, a model predictive control based energy efficient control strategy which aims at maximizing the system efficiency is proposed. Firstly,according to the mass and energy conservation law, an analysis on the nonlinear relationship between superheat and cooling load is carried out, which can produce the maximal effect on the system performance. Then a model predictive control(MPC)based controller is developed for tracking the calculated setting curve of superheat degree and pressure difference based on model identified from data which can be obtained from an experimental rig. The proposed control strategy maximizes the coefficient of performance(COP) which depends on operating conditions, in the meantime, it meets the changing demands of cooling capacity.The effectiveness of the control performance is validated on the experimental rig.
