Development of Fuzzy Logic Control for Indoor Lighting Using LEDs Group

TELKOMNIKA, Vol.16, No.3, June 2018, pp. 1165~1173
ISSN: 1693-6930, accredited A by DIKTI, Decree No: 58/DIKTI/Kep/2013
DOI: 10.12928/TELKOMNIKA.v16i3.8048  1165
Received November 25, 2017; Revised March 30, 2018; Accepted April 11, 2018
Development of Fuzzy Logic Control for Indoor Lighting
Using LEDs Group
M. Khairudin*
1
, S. Riyanto
2
, Z. Mohammed
3
1,2
Department of Electrical Engineering, Universitas Negeri Yogyakarta (UNY), Yogyakarta, Indonesia
3
Faculty of Electrical Engineering, Universiti Teknologi Malaysia (UTM), Johor, Malaysia
*Corresponding author, e-mail: moh_khairudin@uny.ac.id
Abstract
This paper presents the design and the developmentofan indoor lighting control based on fuzzy
logic controller (FLC). The objective of this study is to demonstrate how the FLC can optimize the lighting
based on indoor and outdoor lighting environments.The FLC system uses two inputs with Light Dependent
Resistors (LDR) as a sensor at indoor and outdoor lighting environments. The output is Light Emitting
Diodes (LEDs) to provide lighting atthe room automatically.The Pulse Width Modulation (PWM) is used to
adjustthe LEDs lighting in the room.FLC has successfully demonstrated performance to control the output
of LEDs as needed. If the LDR 1 input as indoor sensor shows dim, it will automatically turn on brightly.
The MSE values for simulation and experiment of LDR 1 and LDR 2 were 34.42 and 30.11 respectively.
The results of FLC performance in the simulation work are further validated by an experimental work.
Experimental results show similarities compared to the simulation results.
Keywords:fuzzy logic control,indoor lighting, LED
Copyright © 2018 Universitas Ahmad Dahlan. All rights reserved.
1. Introduction
The program to realize the green campus with electrical energy saving measures such
as replacing the types of lamps become light emiting diode (LED). The LED type lights are used
of to save electrical energy [1]. LEDs consume very little power while the light produced is high
as well as having a long life time. Therefore, the development of a spatial lighting control system
used the LED by increasing the efficiency of the light time and the use of the lamp based on the
natural lighting around, it became no waste of electrical energy. Effective lighting system
provides better performance to improve equipment efficiency, safety and productivity [2].
The combination of natural lighting from the natural surroundings and the lighting from LED type
lights are controlled by using an intelligent system, it is expected to materialize green rooms.
Utilization and consumption of energy for household use have reached up to 40% [3] and [4].
Paralelly with technology development, the use of energy is always increasing
especially electrical energy. In 2013 to 2015 the use of electricity in Indonesia increased by
7.2%, ie from 84.43 to 90.48 Tera Watt Hour (TWh) [5]. One of the items was caused by
wasteful use of electricity in lighting room especially at homes. During this time, a lot of
switching of lighting system still use a conventional switch to turn on and turn off the lights in the
room. Particularly the lights in the room is always lit with lighting. As for during the day, the
users will turn off manually the lighting because the sun begins to light, this will make the light in
the room directly supplied by sunlight. Several studies have been conducted in a smart lighting
system based on sensors [6] and [7]. Arindam et al [8] used daylight to achieve energy saving
through chopping off peak deman. Furthermore, a fuzzy logic is used to make decision of the
number of light intensity through controlling the number of the room lamps [9]. The result
achieved the design of fuzzy logic system integrated with electronics hardware of automated
light controller to obtain automatic intensity of the room. In the case of building lighting atrium
control, [10] used electric lighting energy and visual performance for an atrium building with high
frequency dimming controls.
The phenomenon of high use of electrical energy in Indonesia must be overcame well,
otherwise there will be an increase in the use of electrical energy. Energy usage should be in
accordance with needs. Frequently there is waste of electricity usage. Furthermore, to obtain
saving energy [11] generated LED based lighting system by using pico hydro. To make saving

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energy, [12] analysed a total electric energy demand based on the effect of building occupancy
concentration using a fuzzy control system. Otherways, to make a respond of the natural state
of daylight, [13] used artificial lighting control based on FLC. Aryanto and Seno [14] studied the
control of the compact fluorescent lamp to switch on or off according to the condition of the
illuminance in the room using FLC with microcontroller. On the effects of using control schemes,
[15] conducted an analysis about these control schemes on lighting system having an unique
set of factors to affect their performance in terms of energy comsumption and lighting quality.
The fact is the use of home lights, garden lamps, street lights still use the conventional
control. During this time the use of natural light energy in everyday life is still not implemented
optimally. With the combination of sunlight harmonious and electronic devices such as lights,
the use of energy in the room, home, public places can become more efficient. Meanwhile [16]
presented a fuzzy logic based lighting system with fluorescent lamp automation to achieve
lighting according to Indonesian National Standard (SNI) to achieve energy savings of 75%.
This paper presents a light intensity room control module using FLC as an effort to
reduce the use of electrical energy. This study presents the FLC to make the lighting system be
automatically light in the room that will be bright and dim in accordance with the outdoors light
(sunlight).The effort is conducted by adjusting the electrical energy requirements requested in
everyday life while utilizing the source of unlimited natural light energy. Natural light that has not
been widely used in this technology will serve as a system stimulant. The lamp as the source of
lighting used in this study is LED. The placement of LDR for detecting the lighting is in two
different places. The first is placed in the room to detect the amount of light, while the second is
placed outside the room to detect the size of natural light. Several works only used the room
lighting for FLC input. This work combines the two conditions namely inside room and outside
room lightings were monitored to control the lighting room based FLC. In this study, a simulation
and experimental works were presented to validate the result. This is rarely conducted by
previous researchers who mostly only conduct FLC design until simulation only. The result
shows FLC performance that meets the standards in automatically setting the LED lights, this is
indicated by the similar result between the simulation and experimental work.
2. Research Method
Figure 1 shows the block diagram of an indoor lighting control system used in this work.
The rig consists of three main parts: a group of LEDs, Light Dependent Resistors (LDR) sensors
and a lab-scale of a room. The indoor lighting control system design consists of a collection of
LEDs as a source of space lighting. The LED as the lighting source in this study amounted to 15
pieces of 5 mm LEDs and arranged circularly centered on the midpoint to obtain optimal light.
As for a lab-scale of the room is constructed in Table 1.
Figure 1. The block diagram of indoor
lighting control
Table 1. Parameters of a lab scale of the room
Parameter Value Unit
Length 0.50 m
Width 0.50 m
Height 0.40 m
Thick of w ood 0.05 m
The sensors used in this study were LDR 1 and LDR 2 which were located at the indoor
side and the outside respectively. The analogue inputs from both LDR was processed through
Analogue to Digital Converter (ADC) that included in microcontroller. Monitoring of system
performance was displayed using LCD monitor. Furthermore, the Atmega16 Microcontroller
used for data processing in this work was to control a lighting.
Data acquisition and control were accomplished by employing the microcontroller that
provided a direct interface between the processor, LEDs and sensors through ADC for analogue
input, analogue dan digital output. Two analogue inputs were required in experimental rig for

TELKOMNIKA ISSN: 1693-6930 
Development of Fuzzy Logic Control for Indoor Lighting... (M. Khairudin)
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input signals of LDR 1 and LDR 2 respectively. The number of 15 LEDs were grouped into three
groups, so three digital outputs were used to supply the group of LEDs respectively. The
software was developed using CodeVision AVR. To assess the functionality and reliability of
lighting control using LDR sensors with FLC algorithm, it is necessary to confirm between
natural lighting from the sun and indoor darkness in the room. The FLC will make an efficient
controller of an indoor lighting based on outside lighting so that there is no wasting
of the lighting.
3. Results and Analysis
The automatic control system for indoor lighting used LDR 1 and LDR 2, otherways the
LED groups were connected to the ouput respectively. The preparation of FLC consisted of four
steps that were determination of fuzzification, dividing in membership function (MF), rule setting
and defuzzification.
3.1. FLC design and simulation
The determination of fuzzy variable in this study was the light intensity as a result of
LDR 1 and LDR 2 input scanning as well as output from LEDs groups. The fuzzy set for LDR 1
and LDR 2 was divided into DARK, DIM, and BRIGHT. While the fuzzy set of LEDs was divided
into MINIMAL, MEDIUM, and MAXIMAL. The determination of fuzzification needed to be
determined the universe of discourse. The universe of discourse was the total value allowed to
be operated in fuzzy variables. The universe of discourse for variables LDR 1 and LDR 2 were
[0 1000] and [0 1000] respectively, while the universe of discourse for variable LEDs was [0
255].The fuzzy set domain was the total value allowed in the universe of discourse and might be
operated in a fuzzy set. A complete domain set fuzzy can be seen at Table 2.
Next step was the determination of membership function. The membership function
(MF) is a curve showing the mapping of data input on a membership value that has an interval
between 0 and 1. MF design for input of LDR 1 and LDR 2 can be seen at Figure 2. MF Design
of LDR 1 and LDR 2 are arranged with the same parameters because it is possible to have the
same characteristics. While the MF design for LEDs groups as ouput in this study is as shown in
Figure 3.
Figure 2. MF design for input of LDR1 Figure 3. MF design for output of LEDs groups
Table 2. Domain set of input and output fuzzy
Variable Dark Dim Bright
LDR 1 [-500:0:500] [0:500:1000] [500:1000:1500]
LDR 2 [-500:0:500] [0:500:1000] [500:1000:1500]
Minimum Medium Maximum
LEDs [-127.5:0:127.5] [0:127.5:255] [127.5:255:382.5]
After the determination of membership function, the fuzzyfication process was
conducted for input and output. In the input type LDR 1, the determination of input variable ( x )
was the value of ADC for example with value of 100. The fuzzyfication determination with the
Tsukamoto model on the dark MF to get the error value (  ) was conducted by using the
following equation (1):
0 500 1000
Dark Dim Bright
1
0 127.5 255
Minimum
Medium
Maximum
1

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





bx
bxaabxb
MFDARK
;0
);/()(
 (1)
with x is the value of the LDR sensor 1 input variable from the ADC, thus b and a are upper
and lower limits respectively. So for DARK MF when given x , a and b with 100, 0 and 500
respectively, then it is obtained 8.0dark
The determination of fuzzyfication in this study was done by using Tsukamoto model, so
to get the error value (  ) in DIM MF can be done by using equation (2). With x is the value of
the LDR 1 input variable from the ADC, then c , b and a are upper and medium and lower
limits respectively. So for DIM MF given x , a , b and c with 100, 0 , 500and 1000
respectively, it will be obtained 2.0dim . While the determination of fuzzyfication on MF
BRIGHT in order to get the error value (  ) is by using equation (3).









cxbbcxc
bxaabax
cxorax
MFDIM
);/()(
);/()(
;0
 (2)









bx
bxaabax
ax
MFBRIGHT
;1
);/()(
;0
 (3)
With x is the value of LDR sensor 1 input variable from ADC, thus b and a are upper and
lower limits respectively. Thus for MF BRIGHT if given x , a and b with 100, 500 and 1000
respectively, then it is obtained 0bright . As for the determination of fuzzyfication with
Tsukamoto model on LDR 2 with the same equation as in LDR 1 in the process of getting the
value of delta_error (  ), then it got as in Table 3.
Table 3. delta_error (  ) value for input of LDR 2
Classification Parameters 
Dark x=600, a=0, b=500 0
Dim x=600, a=0, b=500, c=1000 0.8
Bright x=600, a=500, b=1000 0.2
The next step is the determination of the rule. In this study the determination of the rule
used AND logic. An  predicate as a result of operation by using AND logic operation was
obtained by taking the smallest membership value between elements in the corresponding set
according to equation (4).
))(),(min( yBxABA   (4)
With x and y are the input variable, thehe determination of the rule by using AND logic can be
done with the following equation (5).
conditionoutputTHENconditioninputANDconditioninputIF 21 (5)
So the  predicate value of the rule can be calculated by equation (6) and equation (7).
conditioncondition LDRLDRpredicate 21   (6)
))(2),(1min( xLDRxLDRpredicate conditioncondition   (7)

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with x is the value of sensor input variable of LDR 1 and LDR 2. After completing the
determination of the rule then the next is the defuzzification process. The defuzzification
process can be done by using the following equation (8):
aabpredicatez ii  ))(( (8)
with b and a are upper and lower limits respectively.
The rules and defuzzification process for equation (6), (7) and (8) in this study, where is
ipred is ipredicate , then on R1, R2, and R3 if there are only limits a and b then a and b are
lower and upper limits for maximum output respectively, whereas if there is a limit a , b and c
then a , b and c are lower, medium and upper limits for maximum output respectively. While at
R4 boundary a , b and c are the limit for defuzzification at medium maximum. As for R5 and R6
are for defuzzification on minimal medium. Then R7, R8 and R9 are for defuzzification at
minimum output.
After the defuzzyfication process on each rule is completed, next is determining the
output value (Z) by using Equation (9) follows
987654321
998877665544332211
predpredpredpredpredpredpredpredpred
zpredzpredzpredzpredzpredzpredzpredzpredzpred
Z




 (9)
It obtaines the value of output (Z) of 160.29. This value is used to adjust the size and
width of the PWM pulse on the LED light. The simulation process was done using Matlab and
Proteus. Each variable in the IF-Then-form rule must be represented by a fuzzy set through a
monotonous membership function. As a result, the output of inference results from each rule is
given expressly (crisp) based on α-predicate. The final result is obtained by using weighted
averages. The process of simulation program testing was done by using Proteus.
The simulation work uses Proteus as shown in Figures 4 and 5. Figure 4 describes a
series of Atmega16 microcontroller systems using three ports. PIN A.0 and PIN A.1 are
connected to Input of LDR 1 and LDR 2 respectively. Otherways PORT B and PORT C are
connected to output of LEDs groups and the LCD display respectively. Based on these
simulations, the results obtained were according to the calculations in theory. Thus the program
can run well. The results of the simulation can be seen in Tables 4 and 5.
Figure 4. Electrical circuit for simulation Figure 5. LCD display for error, delta error and PWM
Table 4 shows the results by providing natural light stimulus inside the room in dark
conditions up to bright light while the outdoor light is in a relatively stable dim condition. Table 4
presents the FLC has worked with the indication when the LDR 1 input detects light in the dark
room (range 0 to 300) and the LDR 2 input detects the outdoor light in the dim range then the
LED output will get the maximum supply so it lights up in maximum condition. This condition
occurs in the PWM range that controls the LED output between 127.5 to 203.59.

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While the conditions at which LDR 1 inputs detect light in a dim room (range 300 to 700)
and LDR 2 input detects outdoor light in the dim range, then the LED output is in medium
condition. This condition occurs in the PWM range that controls the LED output between 93.5 to
127.5. As for the conditions when the LDR 1 input detects light in a bright room (range 700 to
900) and LDR 2 input detects a dimly lit outdoor light, then the LED output is in minimum
condition. This condition occurs in the PWM range that controls the LED output between 46.96
to 93.5.
Table 5 shows the result by providing a natural light stimulus outside the room in dark
conditions up to bright light while indoor light is in a relatively stable range of dim
conditions.Table 5 presents the FLC has worked with the indication when the LDR 2 input
detects dark outdoor light (range 4 to 300) and the LDR 1 input detects light in the dim range
room then the LED output will get the medium supply so it lights up in medium condition . This
condition occurs in the PWM range that controls the LED output between 126.96 to 127.5. This
indicates that the change in external light is not significantly influential, although outside the
room shows dark light but if in the room there is still a dim light source then the LEDs will light
up in medium condition. In contrast to LDR 1 input which is very influential on LEDs lamp
changes, if in the room is identified dark then the LEDs will light up maximally.
Table 4. Simulation results with
input of LDR 1.
Inside the Room Outside the Room
PWM
(error) (Delta error)
0 380 203.59
100 460 189.05
200 470 147.99
300 460 127.5
400 450 125.38
500 386 127.5
600 412 119.39
700 452 93.5
800 472 64.41
900 460 46.96
Table 5. Simulation results with
input of LDR 2
Inside the Room Outside the Room
PWM
(error) (Delta error)
500 4 127.5
500 100 127.5
512 200 127.36
524 300 126.96
524 400 126.96
496 500 126.5
432 600 99.13
420 700 86.08
412 800 88.08
440 900 97.56
While the conditions in which LDR 2 inputs detect dimmed outdoor light (range 300 to
700) and LDR 1 input detects indoor light in the dim range, then the LED output is in medium
condition. This condition occurs in the PWM range that controls the LED output between 86.08
to 126.5. As the condition when LDR 2 input detects bright outdoor light (range 700 to 900) and
LDR 1 input detects indoor light in dim range, then LED output is in minimum condition. This
condition occurs in the PWM range that controls the LED output between 86.08 to 97.56.
Furthermore, FLC performance of simulation results are validated with experimental results.
3.2. FLC Implementation and Experimental Results
Experimental results of the FLC controllers implemented on the experimental indoor
lighting are presented in this section. The task of these controllers is to control the lighting of
LEDs groups based on the sensors of LDR 1 and LDR 2 located at indoor and outdoor
respectively. For real-time implementation of FLC controllers, the code Vision AVR to support
compiling and editing the scripts of Atmega16 microcontroller was utilized. This study conducted
the experimental control of room lighting with FLC as in the simulation that consisted of 2 inputs
in the form of LDR 1 and LDR 2 sensors and the output was divided into 3 groups LEDs. The
results of experimental work can be seen in Table 6 and Table 7.
Table 6 presents the performance of FLC with LDR 1 input classification in the fuzzy
domain set namely dark, dim and bright. Meanwhile LDR 2 inputs are provided permanently.
The output of the FLC system in the form of lighting from the lighting of the LEDs is classified in
the fuzzy domain of minimum, medium and maximum with the range of [-127.5: 0: 127.5], [0:
127.5: 255] and [127.5: 255: 382.5] respectively.
Performances of input LDR 1 and LDR 2 and output LEDs are tested by measurement
in light intensity (flux) units. Experimental work is conducted by stimulating various LDR 1 input
values while LDR 2 is stimulated with a fixed value in the fuzzy domain in the dim range. Table

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6 describes when lighting conditions in the darkened room with indicator low light intensity
measured at input LDR 1, then the output will give a high PWM value so that these LEDs will
show the higher light intensity. As a result the value of light intensity measurement will be
higher. Meanwhile, if there is increase of intensity of light read on the LDR 1, it means that
inside the recorded chamber, the illumination becomes brighter then the output will give a low
PWM value so that these LEDs will show the lower light intensity. As a result the value of light
intensity measurement will be lower. It can be concluded that FLC has shown performance in
the classification of input and output. The performance measurement of outdoor light conditions
controlled through FLC can be seen in Table 7.
Table 6. Experimental results with of LDR 1
Inside the Room
(Delta)
PWM
Light Intensity of
LDR 1
Light Intensity of
output
0 211 0 2100
100 189 8 1750
200 146 10 1500
300 121 15 1300
400 118 80 1200
500 127 160 1250
600 115 180 1300
700 82 280 850
800 65 400 800
900 53 1950 400
Table 7. Experimental results with of LDR 2
Outside the
Room (Delta)
PWM
Light Intensity
of LDR 2
Light Intensity
of output
4 128.4 0 1300
100 127 0 1300
200 127 10 1300
300 127 10 1300
400 127 20 1300
500 122 55 1200
600 92 100 950
700 80 420 850
800 75 600 850
900 93 680 950
Table 7 presents FLC performance with LDR 2 input classification in the fuzzy domain
set namely dark, dim and bright. Meanwhile LDR 1 inputs are provided permanently in the dim
range. The output of the FLC system in the form of lighting from the lighting of the LEDs is
classified in the fuzzy domain of minimum, medium and maximum with the range of [-127.5: 0:
127.5], [0: 127.5: 255] and [127.5: 255: 382.5] respectively. The performances of input LDR 1
and LDR 2 and output LEDs are measured in light intensity (flux) units. Experimental testing is
done by stimulating various LDR 2 input values while LDR 1 is stimulated with a fixed value in
the fuzzy domain dim. Table 7 presents the conditions when the outdoor lighting are darkened
by indicators of the low measured light intensity at LDR 2 and the LDR 1 measurement remains
in dim condition, then the output will give high PWM value so that these LEDs will show the
higher light intensity. As a result the value of light intensity measurement will be higher.
Meanwhile, if the increase of intensity of light read on the LDR 2, it means that outside
the recorded chamber illumination, outside the room is brighter, then the output will give a low
PWM value so that these LEDs will show the lower light intensity. As a result the value of light
intensity measurement will be lower. So it can be concluded that FLC has shown performance
in the classification of input and output. It should be noted that external light changes are not
significantly influential, although the outdoors show dark light but when in the room there is still
a dim light source then the LEDs will light up in medium condition. In contrast to LDR 1 input
which is very influential on LEDs lamp changes, if in the room, it is identified dark then the LEDs
will light up maximum. This shows the same thing between the simulation and
experimental results.

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The result of FLC performance test between simulation and experimental work showed
almost the same result and there is only a slight difference. Comparison of FLC performances
between simulation and experimental work for input variable of LDR 1 that shows variable of
light in the room can be seen in Figure 6. While comparison of FLC performances between
simulation and experimental work for input variable LDR 2 or variable of light that exists outside
the room is shown in Figure 7.
Figure 6. Comparison between simulation and
experiment of LDR 1 response
Figure 7. Comparison between simulation
and experiment of LDR 2 response
Figures 6 and 7 show the simulation and experimental results have performance
similarities, it means that FLC has shown the performance in accordance with the design that
has been determined. The comparison between simulation and experimental results is
conducted using the Mean Square Error (MSE) method. The MSE values for simulation and
experiment of LDR 1 and simulation and experiment of LDR 2 were 34.42 and 30.11
respectively.
4. Conclusion
An automatic indoor lighting control system based on FLC is presented. This study
features an FLC control system to assess indoor lighting performance with two LDR 1 and LDR
2 for FLC inputs as well as a set of LEDs as outputs. FLC performance assessing has been
conducted through simulation and experimental works. The results of a performance assess
through simulation shows relatively similar result with experimental work. This shows that the
FLC has shown the performance as expected which can set the high and low level intensity of
room light based on needs. If indoor sensors and outdoor sensors detect the need to increase
the intensity of light, the FLC can meet them as necessary. The results of indoor sensor
detection of lighting requirements give a significant impact on FLC performance to control indoor
lighting output compared to outdoor lighting changes. This makes it easier for users and
streamlines the use of electrical energy.
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Development of Fuzzy Logic Control for Indoor Lighting Using LEDs Group

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Development of Fuzzy Logic Control for Indoor Lighting Using LEDs Group