• Igor Olenych Associate Professor of Radioelectronics and Computer Systems of the Department Faculty of Electronics and Computer Technologies. Ivan Franko National University of Lviv,




lighting control, fuzzy logic controller, production rules, fuzzy sets, conclusions activation.


Background. Modern high-tech automation systems are able to provide unmanned productive and efficient management of smart home functions. These systems should provide control of temperature, light level, humidity and air
pollution for a comfortable stay in the building. In particular, fuzzy logic controller has the potential for application in intelligent systems of lighting control.
Objective.The aim of the paper is to design the two-channel lighting control system in smart home that provides control of lighting source power and of their spectral characteristics.
Methods. The lighting control system is based on fuzzy inference and provides forming the base of fuzzy production rules, fuzzification of input values, aggregation of truth of sub conditions of each rule, activation of conclusions and
defuzzification process that generates an output signal to control the smart home functional devices.
Results. The crisp values of light source power with different spectral characteristics and output signal that controls the transparency of windows have been obtained in result of representation of input data of different types using linguistic variables and fuzzy production rules for the current values of natural light and time of day. It is shown the possibility to change the sensitivity of the control systems in different ranges of illumination deviation from optimal values.
Conclusions. The lighting control method in buildings based on fuzzy logic controller enables to get the quantitative values of power of light sources with different spectral characteristics taking into account the individual characteristics of residents.
Keywords: lighting control; fuzzy logic controller; production rules; fuzzy sets; conclusions activation.


Mendes T.D.P., Godina R., Rodrigues E.M.G., Matias J.C.O., Catalao J.P.S. Smart home communication technologies and applications: wireless protocol assessment for home area network resources // Energies. – 2015. – Vol. 8. – P. 7279–7311.

Harper R. Inside the Smart Home. – London: Springer, 2003.

Zhang D., Shah N., Papageorgiou L.G. Efficient energy consumption and operation management in a smart building with microgrid // Energy Conversion and Management. – 2013. – Vol. 74. – P. 209–222.

Robles R.J., Kim T.-H. Applications, systems and methods in smart home technology: A review // International Journal of Advanced Science and Technology. – 2010. – Vol. 15. – P. 37–47.

Pan M.S., Yeh L.W., Chen Y.A., Lin Y.H., Tseng Y.C. A wsn-based intelligent light control system considering user activities and profiles // IEEE Sensors Journal. – 2008. – Vol. 8. – P. 1710–1721.

Mohamaddoust R., Haghighat A.T., Sharif M.J.M., Capanni N. A novel design of an automatic lighting control system for a wireless sensor network with increased sensor lifetime and reduced sensor numbers // Sensors. – 2011. – Vol. 11. – P. 8933–8952.

Baetens R., Jelle B.P., Gustaven A. Properties, requirements and possibilities of smart windows for dynamic daylight and solar energy control in buildings: A state-of-the-art review // Solar Energy Materials and Solar Cells. – 2010. – Vol. 94. – P. 87–105.

Vergaz R., Pena J.M.S., Barrios D., Perez I., Torres J.C. Electrooptical behaviour and control of a suspended particle device // Opto-Electronics Review. 2007. – Vol. 15. – P. 154–158.

Olenych I.B., Aksimentyeva O.I., Monastyrskii L.S., Pavlyk M.R. Electrochromic effect in photoluminescent porous silicon–polyaniline hybrid structures // Journal of Applied Spectroscopy. – 2012. – Vol. 79. – P. 495–498.

Jin M.-L., Ho M.-C. Labview-based fuzzy controller design of a lighting control system // Journal of Marine Science and Technology. – 2009. – Vol. 17. – P. 116–121.

Panjaitan S.D., Hartoyo A. A lighting control system in buildings based on fuzzy logic // Telkomnika. – 2011. – Vol. 9. – P. 423–432.

Saravanan K., Prabhu N.M., Rajeswari B.R. Fuzzy controller design of lighting control system by using VI package // International Journal of Scientific and Research Publications. – 2014. – Vol. 4. – P. 1–6.

Zadeh L.A. Fuzzy sets // Information and Control. – 1965. – Vol. 8. – P. 338–353.

Kumar V., Kumar S., Kansal H. Fuzzy logic controller based operating room air condition control system // International Journal of Innovative Research in Electrical, Electronics, Instrumentation and Control

Engineering. – 2014. – Vol. 2. – P. 510–514.

Sobhy S.M., Khedr W.M. Developing of fuzzy logic controller for air condition system // International Journal of Computer Applications. – 2015. – Vol. 126. – P. 1–8.

Mamdani E.H. Application of fuzzy algorithms for the control of a simple dynamic plant // Proceedings of the Institution of Electrical Engineers. – 1974. – Vol. 121. – P. 1585–1588.

Besedin P.V., Andrushhak S.V., Kozlov V.K. Fuzzy inference technique in the task of sludge batching management // International Journal of Soft Computing. – 2015. – Vol. 10. – P. 415–419.

Bai Y., Wang D. Fundamentals of fuzzy logic control - fuzzy sets, fuzzy rules and defuzzifications. Advanced Fuzzy Logic Technologies in Industrial Applications. – Springer, 2006.