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Volume 5 Issue 5
Aug.  2018

IEEE/CAA Journal of Automatica Sinica

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Article Navigation >  IEEE/CAA Journal of Automatica Sinica  > 2018  >  5(5): 953-960
Xiaohong Yin and Shaoyuan Li, "Energy Efficient Predictive Control for Vapor Compression Refrigeration Cycle Systems," IEEE/CAA J. Autom. Sinica, vol. 5, no. 5, pp. 953-960, Sept. 2018. doi: 10.1109/JAS.2016.7510250
Citation: Xiaohong Yin and Shaoyuan Li, "Energy Efficient Predictive Control for Vapor Compression Refrigeration Cycle Systems," IEEE/CAA J. Autom. Sinica, vol. 5, no. 5, pp. 953-960, Sept. 2018. doi: 10.1109/JAS.2016.7510250
shu

Energy Efficient Predictive Control for Vapor Compression Refrigeration Cycle Systems

doi: 10.1109/JAS.2016.7510250
  • 1.

    School of Automation and Electronic Engineering, Qingdao University of Science and Technology, Qingdao 266042, China

  • 2.

    Department of Automation, Shanghai Jiao Tong University, and the Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai 200240, China

Funds:

the National Natural Science Foundation of China 61233004

the National Natural Science Foundation of China 61221003

the National Natural Science Foundation of China 61374109

the National Natural Science Foundation of China 61473184

the National Natural Science Foundation of China 61703223

the National Natural Science Foundation of China 61703238

the National Basic Research Program of China (973 Program) 2013CB035500

Shandong Provincial Natural Science Foundation of China ZR2017BF014

Shandong Provincial Natural Science Foundation of China ZR2017MF017

the National Research Foundation of Singapore NRF-2011

the National Research Foundation of Singapore NRF-CRP001-090

More Information
    Abstract
  • 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.

     

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