Skip to main content

Fuzzy Inference Procedure for Intelligent and Automated Control of Refrigerant Charging


Fuzzy logic controllers are readily customizable in natural language terms and can effectively deal with nonlinearities and uncertainties in control systems. This paper presents an intelligent and automated fuzzy control procedure for the refrigerant charging of refrigerators. The elements that affect the experimental charging and the optimization of the performance of refrigerators are fuzzified and used in an inference model. The objective is to represent the intelligent behavior of a human tester and ultimately make the developed model available for the use in an automated data acquisition, monitoring, and decision-making system. The proposed system is capable of determining the needed amount of refrigerant in the shortest possible time. The system automates the refrigerant charging and performance testing of parallel units. The system is built using data acquisition systems from National Instruments and programmed under LabVIEW. The developed fuzzy models, and their testing results, are evaluated according to their compatibility with the principles that govern the intelligent behavior of human experts when performing the refrigerant-charging process. In addition, comparisons of the fuzzy models with classical inference models are presented. The obtained results confirm that the proposed fuzzy controllers outperform traditional crisp controllers and provide major test time and energy savings. The paper includes thorough discussions, analysis, and evaluation.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20


  1. 1.

    Galileo, T.P.: Process Equipment. Website (Mar 2017)

  2. 2.

    AGRAMKOW. Website (Mar 2017)

  3. 3.

    Adler, W.D.: Refrigerator performance testing in the house-hold refrigerator industries today (Mar 2006)

  4. 4.

    Kuijpers, L.J.M., Janssen, M.J.P., Verboven, P.J.M.: The influence of the refrigerant charge on the functioning of small refrigerating appliances. ASHRAE Trans. 94, 813–828 (1988)

    Google Scholar 

  5. 5.

    Radermacher, R., Kim, K.: Domestic refrigerators: recent developments. Int. J. Refrig. 19(1), 61–69 (1996)

    Article  Google Scholar 

  6. 6.

    Damaj, I., Saade, J., Diab, H.: Performance testing of refrigerators using fuzzy inference methodology under labview. In: The 7th IEEE International Conference on Electronics, Circuits and Systems, vol. 2, pp. 17–20 (Dec 2000)

  7. 7.

    Bandarra Filho, E.P., Mendoza, O.S.H., Garcia, F.E.M., Parise, J.A.R.: Energy conservation for refrigeration systems by means of hybrid fuzzy adaptive control techniques. J. Braz. Soc. Mech. Sci. Eng. 38(6), 1753–1766 (2016)

    Article  Google Scholar 

  8. 8.

    Yang, Z., Duan, P., Li, Z., Yang, X.: Self-adjusting fuzzy logic controller for refrigeration systems. In: 2015 IEEE International Conference on Information and Automation, pp. 2823–2827. IEEE (2015)

  9. 9.

    Kocyigit, N.: Fault and sensor error diagnostic strategies for a vapor compression refrigeration system by using fuzzy inference systems and artificial neural network. Int. J. Refrig. 50, 69–79 (2015)

    Article  Google Scholar 

  10. 10.

    You, Y.W., Zhang, Z.G., Guo, C.M., Zhang, L.L.: Optimizing approach to varying load of the refrigeration system based on fuzzy logic control. In: Selected Papers From the 2011 International Conference on Materials Science and Information Technology (MSIT2011). Advanced Materials Research, vol. 433, pp. 6896–6901. Trans Tech Publications (2012)

  11. 11.

    Li, H., Fei, J.: Fuzzy control for a variable speed refrigeration system. In: 2011 Third International Conference on Measuring Technology and Mechatronics Automation (ICMTMA), vol. 2, pp. 272–275. IEEE (2011)

  12. 12.

    Şahin, A.Ş., Köse, İİ., Selbaş, R.: Comparative analysis of neural network and neuro-fuzzy system for thermodynamic properties of refrigerants. Appl. Artif. Intell. 26(7), 662–672 (2012)

    Article  Google Scholar 

  13. 13.

    Rashid, M.M, Islam, A.: Design and implementation of a fuzzy logic based controller for refrigerating systems. In: 2010 International Conference on Computer and Communication Engineering (ICCCE), pp. 1–5. IEEE (2010)

  14. 14.

    Pang, W., Liu, J., Xu, X.: A strategy to optimize the charge amount of the mixed refrigerant for the Joule–Thomson cooler. Int. J. Refrig. 69, 466–479 (2016)

    Article  Google Scholar 

  15. 15.

    Zedeh, L.A.: Outline of a new approach to the analysis of complex systems and decision processes. IEEE Trans. Syst. Man Cybern. 3, 28–44 (1973)

    MathSciNet  Article  Google Scholar 

  16. 16.

    Driankov, D., Hellendoorn, H., Reinfrank, M.: Introduction. In: An Introduction to Fuzzy Control, pp. 1–36. Springer, Berlin (1996).

  17. 17.

    Zak, S.H.: Systems and control. Oxford University Press, New York (2003)

    Google Scholar 

  18. 18.

    Essick, J.: Hands-on Introduction to LabVIEW for Scientists and Engineers. Oxford University Press, Oxford (2013)

    Google Scholar 

  19. 19.

    Measurement National Instruments: Test and Embedded Systems. Website (Mar 2017)

  20. 20.

    Dmitriyev, V.I., Pisarenko, V.E.: Determination of optimum refrigerant charge for domestic refrigerator units. Int. J. Refrig. 7(3), 178–180 (1984)

    Article  Google Scholar 

  21. 21.

    Zaher, A.A.: Design of model-based controllers for a class of nonlinear chaotic systems using a single output feedback and state observers. Phys. Rev. E 75, 056203 (2007)

    Article  Google Scholar 

  22. 22.

    Zadeh, L.A.: Fuzzy sets. Inf. Control 8(3), 338–353 (1965)

    Article  MATH  Google Scholar 

  23. 23.

    Zadeh, L.A.: Fuzzy logic and approximate reasoning. Synthese 30(3), 407–428 (1975)

    Article  MATH  Google Scholar 

  24. 24.

    Klir, G., Yuan, B.: Fuzzy Sets and Fuzzy Logic, vol. 4. Prentice Hall, Upper Saddle River (1995)

    MATH  Google Scholar 

  25. 25.

    Jiang, W., Luo, Y., Qin, X.Y., Zhan, J.: An improved method to rank generalized fuzzy numbers with different left heights and right heights. J. Intell. Fuzzy Syst. 28(5), 2343–2355 (2015)

    MathSciNet  Article  MATH  Google Scholar 

  26. 26.

    Nguyen, H.T., Sugeno, M.: Fuzzy Systems: Modeling and Control, vol. 2. Springer, Berlin (2012)

    MATH  Google Scholar 

  27. 27.

    Hellendoorn, H., Thomas, C.: Defuzzification in fuzzy controllers. J. Intell. Fuzzy Syst. 1(2), 109–123 (1993)

    Google Scholar 

  28. 28.

    Çağman, N., Karataş, S.: Intuitionistic fuzzy soft set theory and its decision making. J. Intell. Fuzzy Syst. 24(4), 829–836 (2013)

    MathSciNet  MATH  Google Scholar 

  29. 29.

    Saade, J.J., Diab, H.B.: Defuzzification techniques for fuzzy controllers. IEEE Trans. Syst. Man Cybern. Part B (Cybern.) 30(1), 223–229 (2000)

    Article  Google Scholar 

  30. 30.

    Wei, Y., Qiu, J., Karimi, H.R.: Reliable output feedback control of discrete-time fuzzy affine systems with actuator faults. IEEE Trans. Circuits Syst. I Regul. Pap. 64(1), 170–181 (2017)

    Article  Google Scholar 

  31. 31.

    Wei, Y., Qiu, J., Karimi, H.R.: Fuzzy-affine-model-based memory filter design of nonlinear systems with time-varying delay. IEEE Trans. Fuzzy Syst. 99, 1–1 (2017)

    Google Scholar 

  32. 32.

    Zhang, H., Wang, J.: Active steering actuator fault detection for an automatically-steered electric ground vehicle. IEEE Trans. Veh. Technol. 66(5), 3685–3702 (2017)

    Article  Google Scholar 

  33. 33.

    Lim, B.C., Horowitz, M.: Error control and limit cycle elimination in event-driven piecewise linear analog functional models. IEEE Trans. Circuits Syst. I Regul. Pap. 63(1), 23–33 (2016)

    Article  Google Scholar 

  34. 34.

    Nejadpak, A., Tahami, F.: Stabilizing controller design for quasi-resonant converters described by a class of piecewise linear models. IEEE Trans. Circuits Syst. I Regul. Pap. 61(1), 312–323 (2014)

    Article  Google Scholar 

  35. 35.

    Wu, D.: Twelve considerations in choosing between gaussian and trapezoidal membership functions in interval type-2 fuzzy logic controllers. In: 2012 IEEE International Conference on Fuzzy Systems, pp. 1–8 (June 2012)

  36. 36.

    Damaj, I., Kasbah, S.: An analysis framework for hardware and software implementations with applications from cryptography. Comput. Electr. Eng. (2017).

    Google Scholar 

  37. 37.

    Damaj, I.W.: A unified analysis approach for hardware and software implementations. In: 2016 IEEE 59th International Midwest Symposium on Circuits and Systems (MWSCAS), pp. 1–4 (Oct 2016)

  38. 38.

    Patterson, D.A., Hennessy, J.L.: Computer Organization and Design RISC-V Edition: The Hardware Software Interface. Morgan Kaufmann, Burlington (2017)

    Google Scholar 

  39. 39.

    Honeywell: Single Gas Valves. Website (Jan 2018)

  40. 40.

    Samadi, P., Mohsenian-Rad, H., Wong, V.W., Schober, R.: Tackling the load uncertainty challenges for energy consumption scheduling in smart grid. IEEE Trans. Smart Grid 4(2), 1007–1016 (2013)

    Article  Google Scholar 

  41. 41.

    VTECH Process Equipment LLC: Refrigarent Charging Equipment. Website (Jan 2018)

  42. 42.

    Wei, Y., Qiu, J., Karimi, H.R., Wang, M.: Model reduction for continuous-time Markovian jump systems with incomplete statistics of mode information. Int. J. Syst. Sci. 45(7), 1496–1507 (2014)

    MathSciNet  Article  MATH  Google Scholar 

  43. 43.

    Wei, Y., Qiu, J., Karimi, H.R., Wang, M.: Filtering design for two-dimensional Markovian jump systems with state-delays and deficient mode information. Inf. Sci. 269, 316–331 (2014)

    MathSciNet  Article  MATH  Google Scholar 

Download references


The authors would like to thank Mr. M. El-Khalili, a Senior Mechanical Engineer and Market Area Director at Eberspcher Strak GmbH, Germany, for the help and advice he provided in some paper-related issues. In addition, the authors are grateful for the thorough reviews, by the editor and the anonymous reviewers, that enabled great improvements to this paper.

Author information



Corresponding author

Correspondence to Issam Damaj.

Appendix: List of Acronyms and Symbols

Appendix: List of Acronyms and Symbols

Acronym or symbol Definition
\(^{\circ }{\hbox {C}}\) Degree celsius
COI Compositional rule of inference
COP Coefficient of performance
CPT Computerized performance test
DAQ Data acquisition
DTemp Temperature change variable
E Energy
GUI Graphical user interface
kWh Kilo Watts per hour
max Maximum
\(\wedge\) Minimum operator
MF Membership function
MISO Multiple input single output
NI National instruments
P Power
PR Performance
S4 Suction tube
t Time
TH Throughput
TpD Tests per 12-h work day
TpH Tests per hour
TS Time saving
TT Test time
Time Observation time variable

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Damaj, I., Saade, J., Al-Faisal, H. et al. Fuzzy Inference Procedure for Intelligent and Automated Control of Refrigerant Charging. Int. J. Fuzzy Syst. 20, 1790–1807 (2018).

Download citation


  • Refrigerant charging
  • Modeling human expertise
  • Performance
  • Fuzzy inference
  • LabVIEW