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Refrigerator Temperature Control Usng Fuzzy LogicAnd Neural Network

Byung-joon Choi Sang-wan Han Suk-kyo HongR&D Center of Refrigerator Robot Application Lab. Robot Application Lab.

Daewoo Electronics Co., Ltd. Ajou University Ajou UniversityIn-Choen Suwon Pdalal-Gu Wonchon Suwon Pdalal-Gu WonchonKOREA KOREA KOREA

~ j cho i @~ a d a ng .a j ~ .a c .r swhan@madang.ajou.ac.kr skhong@madang.ajou.ac.kr

Abstract - This paper describes the quick and precisecontrol method for home-applied refrigerators. Theproposed controller is based on the fuzzy logic andneural network designed for better performance inmaintaining the constant inner temperature of therefrige rator and improving the refrigerator efficiency.The fuzzy logic controller is used to maintain the innertemperature in spite of the environmental variations suchas the outer temperature change or the volume changeof the stored foods of the refrigerator. Neural Networkis used and trained to recognize the user-pattern such asthe number of door opening of the refrigerator. The 1storder approximated model of the refrigerator is used forthe simulation. Through the simulation in the case of theouter temperature change like summer or winter, theproposed fuzzy logic controller with neural network isshown to be more efficient than the conventional on-offcon troller.

L INTRODUCTIONNowadays, refrigerator[11 becomes one of the mostindispensible electric merchandise in every home.Therefore the importance of controlling refrigeratorwhich makes food fresh and sweet should not beoverlooked. To accomplish a reasonable control on

freezing and air conditioning, many researchers haveimplemented the speed control of the induction motorfor compressor on refrigerators using inverterscontrolled by PWM Pulse Width Modulation), andhave studied on the difference between on-off controland continuous control of the freezing speed,temperature and the efficiency of refrigerator. On-offcontrol of the compressor and fan may cause severalcritical problems such as the inefficiency of therefrigerator control and the diEculties of themaintaining the constant temperature. According to thetrend of the large sized refrigerator, that m akes moredifficult to maintain the constant temperature of therefrigerator because the cooling air is outleted throughO-7803-4756-0/98/ 10.00 1998 IEEE 186

the fixed vents. Because of this restricted part ofcooling ar contacts, the freshness of foods isdeteriorated. Another critical problem in this case isthe energy consumption which is the main concern incontrolling the refrigerator. And it is not easy toadopt the inverter control because of its high cost andimplemental difficulties.In this paper, the efficient te m pe ra m controlalgorithm of the refrigerator with Fuzzy Logic andNeural Network[2]. is proposed. The proposed FuzzyLogic controller provides better performance, energysaving, and constant temperature of the refrigerator,and Neural Network classifies the consumer's patternto use the refrigerator. The results of the NeuralNetwork is also used for the other input of the FuzzyLogic Controller. Finally, the approximated modellingof the refrigerator is proposed and evaluated throughthe simulation.

II. CONVENTIONAL TEMPERATURECONTROLLER FOR REFRIGERATOR

The schem atic diagram of the refr igera tor and thecooling air flow is shown in Fig. 1[3]. In general, therefrigerator for home is operated with indirect coolingmethod and separated refrigerating and freezingcompartment whose temperature is maintained 3C and-18C respectively[l]. Basically, Freon R12 and R13are used for the coolant of the refrigerator, butnowadays Freon R-134a which is non-CFC is broadlyused because of the environmental pollution. Thecompressed coolant in the compressor which flowsthrough the heat dissipating pipe and the capillarytube is evaporated in the evaporator and then flowsback to the compressor through the suction pipe[4].The evaporated coolant in the evaporator plays therole of decreasing the evaporator temperature. The fan,installed in front of the evaporator, ventilates the

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Fig. 1 Schematic diagram of therefrigerator

coolin1 ir

desiredtemper tureswitch

Fig. 2 Conventional Temperature Controller of the Refrigerator

cooling ar and refrigerates the food in therefrigerator.The circulation of the cooling air is done by theon-off control of the fan according to the innertemperature of the refrigerator. Typical temperaturecontrol method of the refrigerator is on-off control ofthe compressor and the air blowing fan as shown inFig. 2. The on-off controller in the refrigeratorcompares the real inner temperature with the desiredtemperature and feeds t h i s error signal to the controlinput of the switch block of the controller. Thisconventional on-off control is easy to implement andthe control algorithm is simple but is difficult toadapt the temperature variation due to the outertemperature change or the volume change of thestored foods which results in temperature perturbationand more energy consumption. Above all, the worstthing to overcome in conventional control of therefrigerator is manual adjust of the desiredrefrigerator's temperature by user according to theabove changes. So we propose the new Fuzzy LogicController with Neural Network to overcome thesekinds of problems and d i f f k u l t i es in the followingchapter.

IIL Fuzzy Logic Controller with Neural Network

Fig. 3 Block Diagram of the Fuzzy Logic Controller with NeuralNetwork

The proposed f k z y logic controller with NeuralNetwork is shown i n Fig.3. The main goal of thiscontroller is maintaining the inner temperature of therefrigerator within the admissible range of the errorand automatic controll not to control manually of therefrigerator by user.The error and tlhe error rate between desiredtemperature and the real one are used for the input ofthe fuzzy logic controller[5][6] as shown in Fig. 3.Neural Network reco,@zes the user-pattern based onthe inner temperature and the numbers of the dooropening of the refrigemtor per designated period. Thefuzzy logic controller uses this user-pattern as anotherinput to fuzz the signals. The proposed fuzzy logiccontroller uses singlleton for fuzzification, Sugenofuzzy rule and Sugeno reasoning and COG methodfor defuzzificatioa Also triangular membershipfunction is used for input such as error, error rate,and the user-pattern als shown in Fig.4. The proposedNeural Network is Kohonen Network based oncompetitive learning iule and it matches input vectorto one of the categories adjusted by training set.A. Neural Network

Neural Network b,ased on the modelling of thebiological neural function and architecture of humanbrain consists of several systems which produces theoutputs to result fmm the learning and makes adecision by itself. There are many applicable fields inNeural Network, but especially pattern recognition isthe most commonly used application field. It isdivided into two categories in methodology. when theinput patterns are classified into similar classes, one issupervised learning which has already known the classto be belonged the other is unsupervised learningwhich classrfy the input patterns by itself. Ingeometrical aspect, the unsupervised learning isdefined that its weighting center of class is similarwith the architecture of the trained pattern and incontrast with it, the supervised learning is defined thatthe class of trained pattern is divided by the

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weighting center. In typical, the former is competitionlearning and the letter is back propagation laming.At the beginning stage, the supervised learning like aserror back propagation learning is mainly studied inmany fields. But, this type of learning is slow inlearning its train and indistinct to distinguish the inputpatterns. Also, it is known that there is need ofautonomic learning and it is improper to deal withspeedy problems. To be apposed, because thecompetition learning has a fast learning ability and itis easy to implement the hardware, latest there remany activities in studying the competition learning.The pattern c1assif"ier with N e m l Network is superiorto the past pattern classifier and the merits are asfollow : The first, because of parallel computationwith parallel architecture, it has fast processing speed.The second, it is able to identify the input patternswhich has a little distortion to compare with the testpatterns. The third, it can search for the most similarcategories for the new input patterns. And the forth,there is no need of complex software and itsarchitecture is simple.In this paper, the applied Neural Network is thecompetition learning which is one of the unsupervisedlearning and its analysis is as follow :This network must discover for itself anyrelationships of interest that may exist in the inputdata and translate the discovered relationship intooutputs. The competitive learning rule[Grossberg,1969b; Ruvelhart and Zipser, 19861 is described by

c>0.where si y i ) = I + e-'';And the Kohonen learning ruleEohonen, 19891 orthe winner-take-all learning rule is based on the

clustering of input dat to group similar objects andseparate dissimilar ones. Fig.14 represents the Kohonennetwork.

The output y, is computed by where a( - ) is acontinuous activation function.Y i = a ( W T m , 2)

andThe training set is ( X1, X2,.., X ) whichrepresent n clusters. The Kohonen learning rule is ina specific form of (1) and is described by a twostage computation:

W = ~ 1 . e , w e , - - - , i m 1 T.

Similarity matching

X I i

XP

x3 i

Xm yn

Fig. 4 Kohonen Network

B. Fuzzy Lo ControllerFuzzy logic control rules re as follows :

Rule1 : IF ( error[rate] is NE3 ) and ( NN is ZE )Rule2 : IF error[rate] is NS ) and ( NN is ZE )Rule3 : IF ( error[rate] is ZE ) and ( NN is ZE )Rule4 : IF error[rate] is PS ) and NN is ZE )Rule5 : IF error[rate] is PB ) and NN is ZE )Rule6 : IF error[rate] is ZE ) NN is NB )Rule7 : IF ( error[rate] is ZE ) and ( NN is NS )Rule8 : IF ( error[rate] is ZE ) ( NN is PB )Rule9 : IF error[rate] is ZE ) and NN is PB )

THEN ( output is ONB ).THEN ( output is ONS ).THEN ( output is OZE ).THEN output is OPS ).THEN output is OPB ).THEN output is ONB ).

THEN ( output is ONS ).THEN ( output is OPB ).THEN output is OPB ).

In this control rule, emr[rate] means that iso : x c

Updating : Fig. 5 Type of Membership Function andEquation188

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Table 1 Fundamental Fuzzy Rule Table And the method of defuzzifier is the Center of( NN : Neural Network, ER : Error Rate 1 Gravity method.

NSZEPS

ONSONB ONS 02 OPS OPB

OPS

min. 0 max.a) membership function ofinputNB NS ZE PS PI3

min. 0 max.b ) membership function ofoutputFig. 6 Input, Output Membership Function

change rate of difference between referencetemperature and inner tempera- of the refrigeratorwith time. Error[rate]s are classified into NB, NS, ZE,PS, PB and PB represents positive big, PS : positivesmall, ZE : zero NS : negative small, NB : negativebig respectively. NN is a normalized input to FuzzyLogic Controller which is usergattern to be trainedand chosen by Neural Network. This rule base selectsstate variables based on the reference and calculateserror by way of comparing system output changewith a value chosen by the state variables. And thiserror becomes the input of the fuzzy logic controller.The above rule bases are basic rules. hrough thistask, there are 10 userjattems( 10 outputs of theNeural Network ) and 5 error[rate], total 50 fuzzyrule bases are designed. Fig. 6 shows the input,output membership function of fuzzy logic controller.Finally, it is explained the important compositionalelements of the Fuzzy Logic Controller. The fuzzifierperforms a mapping from a crisp pointsI= x 1 , ..., x . ) T~~ into a fuzzy set A in U.We choose a Singleton fuz zife r : A' is a fuzzysingleton with suppo rt that is, ,u~(xJ=l for allother EU with . .E. A fuzzy rule base consists ofa collection of fuzzy IF-THEN rules in followingform :Rj : IF XI is AI, and ... x, is Aq THEN y is B,,

j = 1, ..,In this case, j is 50. The defuzzifier performs amapping from fuzzy sets in V to a crisp point yV.

IV. SIMULATION AND RESULTSA. Temperature control Method of the Refrigeralor1st order form with time delay as shown in (1)[7][8].

The model of the refrigerator is simplified into theK e r sas = + Ts (4)

Parameters of approx imated model for a refrigeratorsuch as K, T and Z were calculated throughexperiments. According to these parameten, thetransfer function of the approximated model forrefrigerator is 0.69 e - sas 1 175s 5 )

The fundamental concept of the refrigerator modelis based on the thermodynamics and the mainelements are input energy and the amount of heatleakage. The input energy is defined what iscalculated with the enthalpy of the cooling air to flowinto the refrigerating compartment like as (6). Theamount of heat leakage is defined and calculated suchas (7). In (7), K means heat leakage factor, To souter tempera* and T, is inner temperature of therefrigerator respectivel y.

6)nput Energy = ( k nput Aid I hl 1x Enthalpy of AidEUI&l}Q = K x ( To Ti ) 7)

B. Simulation ResultsSimulations are canned out in two cases, with andwithout disturbances. .Also they are carried out in theconsideration of the user pattem which is the usingmethod of the refrigeimtor. Distufiances are treated asthe outer temperature change of the refrigerator or thevolume change of the stored groceries. The resultshown in Fig. 7and Fig. 8is the tempera- of theconventional on-off conml in the case that the outertempera- of the refrigerator is fixed at 30C. Thatcase is as usual in refrigerator. This figure tells usthat the blowing fan for cooling air is activated(oncondition) when the inner temperature of therefrigerator is 2.5 c and deactivated(off condition)when it is 1.5-C respectively. Fig. 10 shows theresults of the convenltional on-off controller and theproposed fuzzy logic controller in the case that theouter tempera- decreases to 15'C. This figureshows that the duration of the on and off time of the

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blowing fan for cooling ar in the fuzzy logiccontroller is longer than those in the conventionalon-off controller. Therefore this means that the fuzzylogic controller is more efficient and also lower powerconsumption t h n the conventional one especially inthe winter season. Fig. 9 shows the result of the casewhen the outer temperature increase up to 35C likein the summer season. In this figure one can see thatthe number of the on and off of the fan is increasedand the lower activation temperature is decreaseddown to -0.3 "C. From these fig&s, the quality of therefrigeration is uniformly maintained through allseasons using fuzzy logic controller. Also, in fig. 11,we can see the effects and abilities of the NeuralNetwork. We expect that if the user frequently usethe refrigerator, the trained output of the N e dNetwork inputs to the Fuzzy Logic Controller andFuzzy Logic Controller adjusts the points of theblowing fan both on and off low down. Fig. 11shows the case which the number of usage of therefrigerator is little. In such case, the trained output ofthe Neural Network also adjusts the points of theblowing fan both on and off higher up. We can seein these results what are the effects and performancesof the Fuzzy Logic Controller and Neural Network.The main role of the proposed controller is to copewith the disturbance which is variance of theatmosphere and the user patterns of the refrigerator.

n5

0 5

00

Time Min.)

Fig. 7 Refrigerator Temperature of the Conventional -OfController:2Time (Min.)

Fig. 8 Refrigerator Temperature Comparing ConventionalControl Between 290 Minuties and 360 Minutes

(The outer temperature of the refrigerator is 30C.)

2 2-.o 2 0-g l a+ 1 6

1 4I

290 3 370 320 330 3 0 350 36Tm. Min )

Fig. 9 Refrigerator Temperature Comparing ConventionalControl and Fuzzy Logic Control

(The outer temperature of the refrigerator is 35C.)

2 8 ....__..cn FuuyFUZZY.-

290 3fa 310 320 330 34 3 360Time ( Min. )

Fig. 10 Refrigerator Temperature Comparing ConventionalControl and Fuzzy Logic Control

(The outer temperature of the refrigerator is 15C.)

290 300 310 320 330 340 350 380Time ( Win. )

Fig. 11 Refrigerator Temperature Comparing ConventionalControl and Fuzzy Logic Control with Neural Network(The outer temperature of the refrigerator is 15C.)

V. CONCLUSIONWe have presented the temperature control method

of a home-applied refrig erato r using fuzzy logiccontroller with Neural Network. Through the fuzzy

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logic control, we have showed that one is able toreduce the inner temperature variation due to thedistuhances which are the outer temperature changeof the re frigerator or the volume change of the storedfoods in the refrigerator. Neural Network is also usedto recognize and train to learn the user-patterns basedon the inner temperahire and the number of dooropening by user of the refrigerator.The 1st order approximated model of therefrigerator temperature control system which is basedon the thermodynamics is calculated throughexperiment, and the simulation results using thismodel shows that the proposed fuzzy logic controllerwith Neural Network operates more efficiently andtherefore it needs lower power consumption than theconventional on-off controller especially when thevariation of the outer temperature of the refrig erato r islarge. Also, we are able to improve the performanceand to venfy the automatic operation of therefrigerator which is no needs of manual operation byuser according to the variation of the atmosphere. Forthe further works, the precise modelling of therefrigerator temperature control system, the hardwareimplementation and the improvement of fuzzy logiccontrol law of this controller with N e d Network areremained.

VI. ACKNOWLEDGEMENT

Application Lab. of tlhe Ajou University, also to themembers, Refrigerator R&D Center of the DaewooElectronics Co., Ltd.

VII. REFERENCES[l] Korea n In dustria l Sta.ndard(KS), "HouseholdElectric Refrigerators, Refrigerator-Freezers andFreezers)", KSC 9305, 1997.[2] C.T. Lin, and C.S.G. Lee, "Neural FuzzySyatems" Prentice-Hall, pp. 1-4, 1996.[3] Daewoo Electronics Co., "Daewoo ElectricRefrigerator Specificalion", EJA- C-ROO 1, 1994.[4] Young-m oo Park; Kyung-keun Park, Ho-myungJang, "Industrial Thenmo-Dynamics"[5] Suk Chae, Yowigsuk Oh, Fuzzy Theory andControl", Chung-moon Gak, pp. 207-251. 1995.[6] Kwang-hyung Lee, Gil-rok Oh, "Fuzzy Theory andApplication", Hong-leimg Science Publisher.[7] Lin Ruisen, Xu Yaoliang, Wang Ruhua, Gao Li,and Lu Iiang, "A New Type of Adaptive TempertureProgramming Control" Proc. of the 1996 IEEE[SI X.H. Ma, H.A.Pi.eisig, and R.M.W ood, "On TheModelling of Heat xchangers For Process Control"Proc. of the 1992: American Cont. Cod. pp.

pp. 359-427, 1995.

IECON, pp. 365-367, 1996.

1441-1442, 1992.The authors thank to the students, Robot

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