Design and Implementation of Genetic Fuzzy Controller for Split Air-Conditioner Control Based on Fanger’s PMV Index

This paper reports the design and implementation of genetically optimized fuzzy logic controller (GAFLC) for split air-conditioner based on the principle of Fanger’s Predicted Mean Vote (PMV) index. The proposed control strategy is aimed at improving the indoor thermal environment (ITE) at houses, offices, libraries, hotels, etc. because it plays a vital role in determining the health, physical and mental productivity of the occupants. The GAFLC has been implemented in MATLAB Simulink for computer simulation and also on hardware platform using the commercially available 8-bit ATmega-328 microcontroller through embedded C-coding for real practice. One part of the designed control algorithm examines the values of activity level, clothing insulation, air velocity, and relative humidity and decides the comfort temperature value to be set such that the PMV and PPD indices get satisfied. The other part generates a control signal to the air-conditioner compressor to maintain that temperature. From the simulation results it is seen that the generated comfort temperature values are in the range of 24.4∘– 26.55∘C for various combinations of environmental and personal parameters, which are well above the general temperature set value of 20∘C. This indicates the scope for reducing energy consumption to a greater extent. Also the PMV index lies in the range of − 0.23 to + 0.36 with untuned fuzzy inference system (FIS), and in the range of − 0.32 to + 0.14 with genetic algorithm (GA)-tuned FIS, which are acceptable comfort levels that human physiology can endure with more satisfaction. The experimental results show that GAFLC has generated a comfort temperature value for specified input parameters and also maintained the room temperature at that value to keep the thermal ambience more satisfactorily.