Abstract:Cold storage refrigeration systems possess the characteristics of multiple input and output and strong coupling, which brings challenges to the optimize control. To reduce the adverse effects of the coupling and improve the overall control performance of cold storage refrigeration systems, a control strategy with dynamic coupling compensation was studied. First, dynamic model of a cold storage refrigeration system was established based on the requirements of the control system. At the same time, the coupling b… Show more
“…Several methods have been used for the control of refrigeration systems. In Ma’s and Bayram’s studies [7,8], fuzzy logic control was applied to control the temperature of a refrigeration system, while in Pedersen’s study [9], a neural network is combined with a gain scheduling-based PI controller to control the overheating of a refrigeration system. In addition, in Yin’s and Schalbart’s studies [10,11], MPC controllers are utilized to control refrigeration systems, and a L-Band SBQP-Based MPC control scheme has also been applied to control two different devices in a supermarket refrigeration system [12].…”
The recent decades have witnessed refrigeration systems playing an important role in the life of human beings, with wide applications in various fields, including building comfort, food storage, food transportation and the medical special care units. However, if the temperature is not controlled well, it will lead to many harmful public health effects, such as the human being catching colds, food spoilage and harm to the recovering patients. Besides, refrigeration systems consume a significant portion of the whole society’s electricity usage, which consequently contributes a considerable amount of carbon emissions into the public environment. In order to protect human health and improve the energy efficiency, an optimal control strategy is designed in this paper with the following steps: (1) identifying the refrigeration system model based on a least squares method; (2) tuning an initial group of parameters of the proportional-integral-derivative (PID) controller via the pidTuner Toolbox of Matlab; (3) using an intelligent algorithm, namely fruit fly optimization (FOA), to further optimize the parameters of the PID controller. By comparing the optimal PID controller and the controller provided in the reference, the simulation results demonstrate that the proposed optimal PID controller can produce a more controllable temperature, with less tacking overshoot, less settling time, and more stable performance under a constant set-point.
“…Several methods have been used for the control of refrigeration systems. In Ma’s and Bayram’s studies [7,8], fuzzy logic control was applied to control the temperature of a refrigeration system, while in Pedersen’s study [9], a neural network is combined with a gain scheduling-based PI controller to control the overheating of a refrigeration system. In addition, in Yin’s and Schalbart’s studies [10,11], MPC controllers are utilized to control refrigeration systems, and a L-Band SBQP-Based MPC control scheme has also been applied to control two different devices in a supermarket refrigeration system [12].…”
The recent decades have witnessed refrigeration systems playing an important role in the life of human beings, with wide applications in various fields, including building comfort, food storage, food transportation and the medical special care units. However, if the temperature is not controlled well, it will lead to many harmful public health effects, such as the human being catching colds, food spoilage and harm to the recovering patients. Besides, refrigeration systems consume a significant portion of the whole society’s electricity usage, which consequently contributes a considerable amount of carbon emissions into the public environment. In order to protect human health and improve the energy efficiency, an optimal control strategy is designed in this paper with the following steps: (1) identifying the refrigeration system model based on a least squares method; (2) tuning an initial group of parameters of the proportional-integral-derivative (PID) controller via the pidTuner Toolbox of Matlab; (3) using an intelligent algorithm, namely fruit fly optimization (FOA), to further optimize the parameters of the PID controller. By comparing the optimal PID controller and the controller provided in the reference, the simulation results demonstrate that the proposed optimal PID controller can produce a more controllable temperature, with less tacking overshoot, less settling time, and more stable performance under a constant set-point.
Temperature control is an important factor which influences the accuracy of
refrigerant heat transfer experimental results. In this paper, the three
temperature control methods for the electric heating water tank (EHWT) in the
single tube heat transfer experimental rig are investigated. The error of PID
controller is ? 1?C and the stability time is 390s. The control performance is
not satisfactory. A fuzzy controller and a fuzzy PID controller are designed
to improve temperature control performance. The designed controllers are
simulated by Matlab/Simulink and and the results prove that the designed
controllers is suitable for EHWT. The experimental results show that the
performance of the designed controllers are improved concerning. The error of
two controllers is ?0.1?C. Compared to the PID controller, the stability time
of the fuzzy controller and the fuzzy PID controller are decreased by 14.9%
and 43.1%; the overshoot of the two controllers are reduced by 100% and
62.5% , respectively. The results and the control method have great
significance for the refrigerant heat transfer experiment.
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