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1 Automated Light Controller Using Fuzzy Logic This thesis is presented to the Graduate School In fulfilment of the requirements for Master of Science (Intelligence System) Universiti Utara Malaysia By Morad Ali Ambarem Saleh Copyright © Morad Alwerfally, June 2008. All rights reserved.
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Page 1: Morad Ali Ambarem Saleh

1

Automated Light Controller Using Fuzzy Logic

This thesis is presented to the Graduate School

In fulfilment of the requirements for

Master of Science (Intelligence System)

Universiti Utara Malaysia

By

Morad Ali Ambarem Saleh

Copyright © Morad Alwerfally, June 2008. All rights reserved.

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This study describes the implementation of fuzzy logic in designing fuzzy

automated light controller. The fuzzy controller controls the number of lamps

lighted up based on the number of people inside the room. Its main objective is

to demonstrate how fuzzy logic can minimize the number of lamps used and

therefore reduce the electricity consumption. In this study, fuzzy logic

controller has been implemented and tested to predict the behaviour of the

controller under different light conditions by monitoring the membership

function parameters. In a conventional light controller, the lamps change

according to user’s specification. The light will remain on if the user forgets to

switch off the light. Even if an automated light controller exist, at most the

system can only be controlled as on and off without being able to adapt with

dynamic inputs. Fuzzy logic offers a better method than conventional control

methods, especially in the case of counting the number of people and how

much the light intensity is needed. In this study, fuzzy logic has the ability to

make decision as to how much the light intensity is needed by controlling the

number of lamps in the room according to the number of people who have

entered or left the room. On the other hand, the conventional light controller

does not have the ability to solve this kind of issues. It would be more practical

to let more lamps "on" if the light intensity needed is very bright. A

conventional method controller for this decision is difficult to find while fuzzy

logic controller simplifies the task. This study has achieved its objective, which

is to design a fuzzy logic system integrated with hardware circuit of automated

light controller using fuzzy logic to control light intensity in a room. In this

study, tests cases have illustrated that fuzzy logic control method could be a

suitable alternative method to conventional control methods that could save

electricity consumption and offers ease of use to human being.

ABSTRACT

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TABLE OF CONTENTS

Permission to Use ………………………………………………………………………. i

Abstract (Bahasa Melayu) ………………………………………………………………ii

Abstract (English) ………………………………………………………………………iii

Acknowledgment …………………………………………………………………….... iv

Table of Contents ………………………………………………………………………v

List of Figures …………………………………………………………………………..ix

List of Tables ………………………………………………………………………….. x

List of Abbreviations ………………………………………………………………….. xi

CHAPTER 1: INTRODUCTION

1.0 Background ………………………………………………………………...…………1

1.1 Problem Statement …………………………………………………………………....4

1.2 Objectives of The Study ……………………………………………………………....5

1.3 Research Question………………………………………………………………….....5

1.4 Significance of The Study………………………………………………….............…5

1.5 Scope of The Study ………………………………………………………...………...6

1.6 Thesis Overview ……………………………………………………………………...6

CHAPTER 2: LITERATURE REVIEW

Fuzzy Logic ………………………………………………………………………............8

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Decision Support………………………………………………………………………....12

Embedded Systems …………………………………………………………………...…15

Conclusion ………………………………………………...………………………….…20

CHAPTER 3: METHODOLOGY

3.1 Introduction ………………………………………………………………………….21

3.2 Fuzzy Logic Systems………………………………………………………………...24

3.3 Software Development ………………………………………………………………26

3.3.1 Fuzzification……………………………………………………………….27

3.3.2 Fuzzy Inference ………………………………………………………….29

3.3.3 Defuzzification……………………………………………………………..31

3.4 Integration of Software ……………………………………………………………...33

3.4.1 Fuzzy Logic Controller Design…………………………………………….35

3.4.2 System Design……………………………………………………………..36

3.4.3 Circuit Design……………………………………………………………...39

3.4.3.1 Microcontroller AT89C52……………………………………….39

3.4.3.2 Transmitter and Receiver Circuits (TX & RX)………………….41

3.5 Hardware Circuit Development …………………………………………………......44

3.5.1 Hardware Implementation…………………………………………………45

3.5.1.1 Personal Computer……………………………………………….46

3.5.1.2 Parallel Port Cable (LPT)………………………………………...46

3.5.1.3 Infrared Sensor (Receiver & Transmitter)……………………….47

3.5.1.4 Transmitter Circuit………………………………………………48

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3.5.1.5 Reciever Circuit………………………………………………….49

3.5.1.6 Microcontroller Circuit…………………………………………..50

3.6 Interface Microcontroller and Test the design ………………………………………52

3.6.1 Transmitter Circuit Interface……………………………………………….52

3.6.2 Receiver Circuit……………………………………………………………52

3.6.3 Receiver and Transmitter Circuits…………………………………………53

3.6.4 Microcontroller Circuit…………………………………………………….54

3.6.5 The whole Circuit of Automated Light Controller using Fuzzy Logic……54

CHAPTER 4: RESULTS AND DISCUSSION

4.1 Fuzzificatoin………………………………………………………………..……55

4.2 Fuzzy Inference……………………………………………………………...…...62

4.3 Defuzzification………………………………………………………………...…66

CHAPTER 5: CONCLUSION AND RECOMMENDATIONS

5.1 Conclusion …………………………………………………………………………..72

5.2 Recommendations …………………………………………………………….…….73

REFERENCES

References ………………………………………………………………………………74

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LIST OF FIGURES

PAGE

Figure 3.1 Conventional Design and Fuzzy Logic Controller Design……………………...22

Figure 3.2 The flowchart of the work flow…………………………………………............23

Figure 3.3 Components of a Fuzzy System …………………………………………..……24

Figure 3.4 The generic structures of an automated light controller using fuzzy logic……...24

Figure 3.5 The Structure of a Fuzzy and Control System…………………………………..27

Figure 3.6 Basic Fuzzy Logic Design for Automated Light Controller using Fuzzy Logic..27

Figure 3.7 Membership Function of No. of people (Variable 1)…………………………...29

Figure 3.8 Membership Function of Light Intensity (Variable 2)…………………………..29

Figure 3.9 The output of Defuzzification…………………………………………………...32

Figure 3.10 The Defuzzification Code……………………………………………………….32

Figure 3.11 The integrated FL and Hardware for the Automated Light………………………33

Figure 3.12 The flow of integrated system…………………………………………………..34

Figure 3.13 Fuzzy Logic Control Design…………………………………………………….35

Figure 3.14 Detail FLC System………………………………………………………………35

Figure 3.15 Context Diagram………………………………………………………………...36

Figure 3.16 Lamps control system…………………………………………………………...36

Figure 3.17 No. of people in system…………………………………………………………37

Figure 3.18 No. of people out of system……………………………………………………..37

Figure 3.19 Lamps fuzzy system……………………………………………………………..37

Figure 3.20 Light control system…………………………………………………………….38

Figure 3.21 No. of people in system…………………………………………………………38

Figure 3.22 No. of people out of system……………………………………………………..39

Figure 3.23 Light Fuzzy System……………………………………………………………..39

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Figure 3.24 Microcontroller System…………………………………………………………40

Figure 3.25 :No. of people in system (microcontroller)……………………………………..40

Figure 3.26 No. of people out of system (microcontroller)………………………………….41

Figure 3.27 TX and RX circuit A System……………………………………………………41

Figure 3.28 TX circuit A……………………………………………………………………..42

Figure 3.29 RX circuit A……………………………………………………………………..42

Figure 3.30 TX and RX circuits A Fuzzy System…………………………………………...42

Figure 3.321 TX and RX circuit B System……………………………………………………43

Figure 3.32 TX circuit B……………………………………………………………………..43

Figure 3.33 RX circuit B……………………………………………………………………..43

Figure 3.34 TX and RX circuits B Fuzzy System……………………………………………44

Figure 3.35 Block Diagram of Fuzzy Control Lighting System…………………………….45

Figure 3.36 Hardware Architecture………………………………………………………….45

Figure 3.37 Structure of LPT………………………………………………………………...47

Figure 3.38 Infrared Sensor…………………………………………………………….…….47

Figure 3.39 IR Transmitter Circuit…………………………………………………………...48

Figure 3.40 IR Receiver Circuit……………………………………………………………...49

Figure 3.41 Microcontroller Circuit Diagram…………………………………….………….50

Figure 3.42 The whole Circuit Diagram…………………………………………….……….51

Figure 3.43 Transmitter Circuit Interface………………………………………………..…..52

Figure 3.44 Receiver Circuit Interface………………………………………………...……..53

Figure 3.45 Receiver and Transmitter Circuits Interface…………………………………….53

Figure 3.46 Microcontroller Circuit Interface………………………………………………..45

Figure 3.7 The Circuit of Automated Light Controller using Fuzzy Logic Interface ……...55

Figure.4.1: Membership Function graph for No. of People…………………………………56

Figure.4.2 Membership Function graph for Light intensity………………………………..56

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Figure.4.3 The Fuzzification Phase…………………………………………………………57

Figure.4.4 Fuzzification Test1: NP=3, LI=2……………………………………………….57

Figure.4.5 Membership Function graph for NP=3………………………………………….58

Figure.4.6 Membership Function graph for LI=2………………………………………….58

Figure.4.7 Fuzzification Test2: NP = 4, LI = 2……………………………………………..59

Figure.4.8 Membership Function graph for NP=4…………………………………………59

Figure.4.9 Membership Function graph for LI=2…………………………………………..59

Figure.4.10 Fuzzification Test3: NP = 4, LI = 4……………………………………………..60

Figure.4.11 Membership Function graph for NP=4………………………………………….60

Figure.4.12 Membership Function graph for LI=4…………………………………………..60

Figure.4.13 Fuzzification Test4: NP = 6, LI = 6……………………………………………..61

Figure.4.14 Membership Function graph for NP=6………………………………………….61

Figure.4.15 Membership Function graph for LI=6…………………………………………..61

Figure.4.16: FAM Table Algorithm…………………………………………………………..63

Figure.4.17 FAM Table Test1: NP=3, LI=2…………………………………………………64

Figure.4.18 FAM Table Test2: NP=4, LI=2…………………………………………………65

Figure.4.19 FAM Table Test3: NP=4, LI=4…………………………………………………65

Figure.4.20 FAM Table Test4: NP=6, LI=6…………………………………………………66

Figure.4.21 The Defuzzification Phase………………………………………………………67

Figure.4.22 Defuzzification Membership Function Test1…………………………………...67

Figure.4.23 Defuzzification Test1: LI = 2 = LOW…………………………………………..68

Figure.4.24 Hardware Design Test1= 2lamps………………………………………..………68

Figure.4.25 Defuzzification Test2: LI = BRIGHT…………………………………………...69

Figure.4.26 Hardware Design Test2 = 3lamps……………………………………………….69

Figure.4.27 Defuzzification Test3: LI = BRIGHT…………………………………………...70

Figure.4.28 Hardware Design Test3 = 3lamps……………………………………………….70

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Figure.4.29 Defuzzification Test4: LI = BRIGHT…………………………………………..71

Figure.4.28 Hardware Design Test3 = 4lamps………………………………………………71

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LIST OF TABLES

PAGE

Table 3.1 Rule Block (FAM Table)…………………………………………………...30

Table 3.2 Light Intensity (Membership Function Relative Membership)……………31

Table 4.1 FAM Table………………………………………………………………….62

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CHAPTER 1

INTRODUCTION

This section briefly presents the background, problem statement, objective, research question,

significance and scope of study. The main idea of this study is to implement Fuzzy logic in

lighting control and as an alternative method of conventional lighting method.

1.0 BACKGROUND

A control system is a device or set of devices to manage, command, direct or regulate the

behavior of other devices or systems. A control system combinations of components (electrical,

mechanical, thermal, or hydraulic) that act together to maintain actual system performance close

to a desired set of performance specifications. In recent years, control system dependability has

received much attention with the increase of situations where the systems that are controlled by

computer such as home control systems are used (Izumikawa et al. 2005).Neil (2004) defines

control system as an interconnection of components to form a system configuration which will be

provided or (supply) the required system response. Control is automatic unless if it is not

accomplished by manual (human) effect. One of the most common home control systems is

lighting control.

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Lighting controls, addressing controls for electric lighting, offer desired illuminance at

appropriate times while reducing energy use and operating costs of lighting system. Lighting

control systems are an important method for reducing energy consumption and moderating peak

demand in commercial buildings (Francis et al. 1993). Lighting control systems provide building

operators with the means to manage the way lighting energy is used in buildings more efficiently.

These systems use various control strategies to reduce wasted hours of lighting in unoccupied

spaces, automatically adjust electric light levels in synchrony with available daylight or age-

related changes in luminaries output or selectively shed lighting loads to moderate peak demand

(Cziker et al. 2007).

Fuzzy logic is a form of logic used in some expert systems and artificial intelligence applications.

Such logic is originated in 1965 by the scientist Lotfi Zadeh from the University of California.

Fuzzy logic is much more general than conventional logical systems. The main generality of

fuzzy logic is needed to solve the complex problems in the realms of search, question-answering

decision and control. It provides a Foundation to develop new tools to deal with natural language

and the representation of knowledge. Among these tools are: Computing with Words (CW);

Precisiated Natural Language (PNL); Computational Theory of Perceptions (CTP); Proto form

Theory (PT); Theory of Hierarchical Definability (THD); Perception-Based Probability Theory

(PTp); Unified Theory of Uncertainty (UTU) (Zadeh, 2004).

Fuzzy logic has been applied in different fields, such as the control mechanism and decision

support. The fundamental idea in the applications of the fuzzy logic is that people are capable of

decision-making using inaccurate or uncertain knowledge, while algorithms of conventional

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computer require accurate information. Fuzzy logic provides a means representation of human

knowledge.

Fuzzy logic has been proved to be very powerful in the disciple of system control such that the

controller is defined by collection of fuzzy if-then rules (Cristina et al. 2002). There are many

motives that prompted scientists to develop the science of Fuzzy Logic with the evolution of

computer and software that can deal with inaccurate information. Fuzzy Logic (FL) is a very

powerful technique that has been used successfully in control systems such as automated washing

machine, cameras and others. Fuzzy Logic controller (FLC) has proved that it is a suitable

alternative to conventional control algorithms such as PID controllers in terms of flexibility and

fast reactions, simplest design, and good performance (Philip , 2007).

.

An automatic lighting control system is used to automatically switch off unneeded lighting.

Automatic lighting control systems by turn on and off the lights for a specific time, or will be

dimmed depending on some external factors that is aimed to save energy costs and increase lamps

life ( Hydro, 1990).

1.1 PROBLEM STATEMENT

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Conventional control design methods use mathematical models for system development including

its input in designing controllers. Fuzzy control refers to the control of process through linguistic

descriptions (Tsoukals & Uhrig, 1997). The conventional Fuzzy logic is easy to perform in

industry due to its simple control structure, ease of design and inexpensive cost (Wai et al., 2004).

Based on Fuzzy logic studies, adaptive Fuzzy logic control given better performance than Fuzzy

logic control ((Wang, 1994), (Lin & Yang,200),(Jee & Koren, 2004), (Woo et al, 200)). To

control the controller, several adaptive Fuzzy techniques have been suggested such as

membership function tuning, output and input scaling factors tuning and linguistic rule tuning

(Liang, 2002). In this study, tuning is performed on the membership function.

To investigate the effect of fine tuning of membership function, several electrical equipments

have been considered. Designing integrated circuit for the electrical equipments could be very

time consuming. Therefore, lighting control has been considered due to its complexity and the

time frame given to investigate the problem.

1.2 OBJECTIVE

The objective of this research is to use fuzzy logic system for flexibility and fast reactions result

for automated light controller. The specific objectives are:

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• To design the circuit required for implementing automated light controller using fuzzy

logic

• To test the functionality of fuzzy logic implemented in integrated circuit which designed

in chapter 3.

1.3 RESEARCH QUESTION

Based on the research objectives, the research question is:

• How does Fuzzy Logic controller circuit design can be implemented and tested the

functionality of the integrated circuit?

1.4 SIGNIFICANCE

• As the light is automatically controlled, the use of the electricity is also controlled by the

engine. In other words, when nobody is around to use the light, the controller will break

the electric circuit and therefore the light will be off. This implies that unnecessary use of

electricity when nobody is around is somehow produced.

• The other advantages offer by the study is that the system assists the human in improving

the ease of use of the electrical lighting and indirectly this leads to electricity saving.

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1.5 SCOPE AND LIMITATION

• In this study, the tuning is only graphs. Performed on the membership function the future

improvement can be mode on other parameters such as other types of membership

function graph.

• The number of lamps is limitation to 7 only. This could be further increased based on the

complexity of the problem.

• The system is only meant for non-wireless application.

1.6 THESIS OVERVIEW

This project presents in detail in the following chapters. The first Chapter is an introduction

chapter that explains the background overview, problem statements, objectives, significance of

this study and scope. Literature reviews about fuzzy logic, decision support and embedded

systems in second chapter. Third chapter discusses methodology in six phases: fuzzy logic

system, software development, integration of software and microcontroller hardware, hardware

circuit development, and interfacing the PIC with the hardware that will include the testing and

evaluation of the system. Fourth chapter will reveal the results of the project discoveries. The

conclusion of this project is discussed in chapter 5.

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CHAPTER 2

LITERETURE REVIEW

This work is derived from existing research in a number of related areas including fuzzy logic,

decision support, and embedded systems. This chapter contains relevant review of related

literature to the study domain.

2.1 Fuzzy logic

Fuzzy logic is never a new concept, Lotfi Zadeh has introduced fuzzy set theory as far back as

1965 (Yager & Zadeh, 1992). According to Goonatilake and Khebbal (1995), Zadeh developed

mathematics of fuzzy logic to provide a tool for reasoning an approximate (imprecise) model

rather than exact (precise) one. Despite the fact that fuzzy logic also allows statement to be

represented in two states as true of false as in the traditional binary or boolean logic, it is still

differs from either the Boolean or binary representation since it uses human understandable

linguistics term in expressing the knowledge of the system (Oala, 1994). Fuzzy logic has

contributed immensely to the development of artificial intelligence in producing intelligent

machines which invariable has gone a long way in promoting the concept of knowledge-driven

system ( Friedlob & Sehleifer, 1994).

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Fuzzy logic is a paradigm for an alternative design methodology, which can be applied in

developing both linear and non-linear system for embedded control. By using Fuzzy logic,

designer can realize lower development costs, superior features, and better and product

performance. Furthermore, products can be brought to market faster and more cost effectively

(Aptronix Inc., 1996).

Similarly, in the Zadeh’s proposed set generalization theory where membership of some elements

are more thoroughly defined than others. The degree of membership in a particular set may

assume various values ranging from zero to maximum value where zero value denotes complete

exclusion and maximum value indicates complete membership. A good example of fuzzy logic

application are more prominently found in medical field where expert system is developed for

the treatment of certain ailments like collagen disease and pneumonia ( Godo et al., 2000) and

Clinical Practice Guidelines (Liu & Shefman, 1996).

The rules used in fuzzy logic found their root from the natural language. The rules are closer to

human reasoning. A single fuzzy rule might represent several conventional rules. The fact that

fuzzy logic creates a control platform by combining a number of rules and fuzzy variables states,

system control can be achieved despite the fact that , the mathematical behaviour of the system is

incomplete. The following criteria were set out for the applicability of fuzzy logic ( Gradojevic et

al., 2001):

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• The variables involved must be continuous and non-discrete in nature.

• There must not be a specific mathematical model

• The relationship between input and output must be non-linear

• There must be clearly stated rules explaining input-output dependence

The process in fuzzy logic involves formalizing the symbolic processing of fuzzy linguistic terms,

like excellent, good, fair and poor, which are associated with differences in an attribute describing

a feature (Mandel, 1995). Virtually all decision making processes in the real world takes place in

an environment where the goals, the constraints and the consequences of possible actions are not

clearly stated. Several linguistic terms can be used at a particular instance of fuzzifying a system.

Fuzzy logic intrinsically represent notions of similarity, since good is closer and more similar to

excellent that it is to poor (Waston, 1998). Garibaldi (1997) stated that in multi-valued logic, truth

values are represented by asingle real number in the interval [0,1] where 0 represents false, 1

represents true and intermediate values between 0 and 1 (i.e 0<value<1) represents partial truth.

Whereas in fuzzy logic true and false are represented by fuzzy subsets over the interval [0,1] with

arbitrary fuzzy subsets representing other intermediate values.

The conventional control system and fuzzy system are quit alike. The only difference is that a

fuzzy system contains a “Fuzzifier’ which converts inputs into “Fuzzified variables” and a

“Defuzzifier” which converts the output of Fuzzy control process into numerical value output (

Gradojevic et al., 2001). In a Fuzzy system the process of generating the output (control) starts

with accepting inputs, fuzzifying the accepted inputs, and finally executing all the rules from the

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active rules base. The output of the active rules is generated in single form which reproduces a

new output after defuzzification.

Information from Aptronix Inc., (1996) revealed that, fuzzy logic has been gaining increasing

acceptance in the past few years. There are over two thousand commercially available products

developed based on the concept of Fuzzy logic, ranging from washing machines to high-speed

trains. Nearly every application has some potential elements of fuzzy logic which contributes to

the quality of the product such as performance, simplicity, reduced cost and efficiency. It has

become almost impracticable to produce a commercially valuable product without introducing

some elements of fuzzy logic. Since invention, fuzzy logic has recorded unprecedented successes.

It has proved to be a very useful technique in solving various problems in a diverse domain,

though its popularity is very recent. The imprecision features of fuzzy system makes it to be more

applicable in decision system; this makes it to be able to accommodate the ambiguity component

of human expression, such that the vagueness and uncertainty contained in such an expression

can be modeled in the fuzzy sets, and a pseudo-verbal representation, similar to an expert’s

formulation, can be achieved ( Hasilogu et al., 2003). Fuzzy logic avoided the abrupt change

from one discrete output state to another when the input is changed only marginally. This is

achieved by a quantization of variables into membership functions (Herrmann, 1995).

2.2. Decision Support

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Fuzzy logic has been practically applied to quite a number of decision-support system. It is

particularly suitable for medical decision support system. The knowledge-based expert systems

have been developed in medicine after the introduction of Lotfi Zadeh of fuzzy logic; a good

example is the MYCIN system. The knowledge-based medical expert systems have witnessed an

unprecedented growth shortly after the introduction of MYCIN. Practically each of these systems

attempts to deal with unconvinced, by directly applying fuzzy logic. The elements of uncertainty

in medical decision-making are fuzzified by the fuzzy logic concept to be able to provide a

reliable medical decision-support system through theoretical framework for medical expert

systems (Donna & Mauric, 1994).

The intensive care medicine often involves making quick decisions based on a wide variety of

information. To make medical decisions, intensive care unit (ICU) doctors often rely on

conventional wisdom and personal experience to reach a subjective assessments and judgments.

This requires intuitively, or unclear weighting of different factors to achieve an ideal balance

between clinical end points that are often competing. Recently there has been concern about the

increasing burden of unwanted considerable variation in clinical practice. As a result, physicians

are increasingly asked to join the explicit guidelines that were agreed upon by the medical

community at large. Consequently, there is growing interest in computer-based decision support

tools to automate aspects of medical decision-making taking place in complex clinical areas such

as the ICU. However, Fuzzy logic methods are capable of solving this prevailing problem by

producing efficient algorithms that can accommodate the uncertainty nature of clinical-decision

making (Jason & Michael, 2003).

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Garibaldi (1999) presented the application of fuzzy expert system technique in interpreting the

acid-based balance of the blood in the umbilical cord of new born infants. The Spearman Rank

Order Correlation statistics was used to assess and to compare the performance of a commercially

available expert system, an initial fuzzy expert system was compared against a tuned fuzzy expert

system with experience clinicians. The study revealed that, without tuning, the performance of the

crisp system was significantly better (correlation of 0.80) than the fuzzy expert system

(correlation of 0.67) the performance of the tuned fuzzy expert system was better than the crisp

system and effectively indistinguishable from the clinicians (correlation of 0.93) on training data,

and was the best of the expert systems on validation data. This further confirms the suitability of

fuzzy logic in building medical decision-support system.

The development of computerized systems in medicine and biology, had faced several obstacles,

and one of the most prominent problems was the inherent uncertainty. Life sciences are not

amenable computational solutions for the following reasons:

• The lack of full understanding of the mechanisms of biological organisms, and the

inability to obtain complete information state of the organism.

• Lack of precision with ranges of normal physiological parameters and values, and

complications arising from the interaction of several physiological systems functioning

simultaneously.

The complex nature of human made it extremely difficult to model. There are had been series of

effort in the medical field towards developing medical decision-support systems. These systems

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have taken steps in the modeling of complex problems and producing practical support decision-

making process (Metin et al., 1997). .

According to Marks et al. (1995) the system of sustainable farming must be considered, in order

to meet some standards ranging from economical, environmental and social. The three methods

of Multiple Criteria Decision Making (MCDM) are described to represent a range of problems

that may arise using conventional MCDM methods. These problems include: (1) the treatment of

incommensurate units; (2) the ranking procedure for a solution; and, (3) the degree of

discrimination between attribute values. The system of sustainable farming must be considered,

some of the economic, environmental and social standards are required to be met in order to have

a more profitable farming process.

2.3. Embedded Systems

Fuzzy logic is a powerful problem-solving methodology with a wide range of applications in

embedded control and information processing. Fuzzy logic provides a remarkable simple way to

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drawing conclusion from vague, ambiguous or imprecise information. In a sense, fuzzy logic

resembles human decision making with its ability to work from approximate date and find precise

solution (Awad, 1996).

Fuzzy logic has been found to be suitable for embedded control applications. Several

manufactures in the automotive industry are using Fuzzy technology to improve product’s quality

and reducing development time. In aerospace, Fuzzy enables very complex real time problems to

be solved using a simple approach. In consumer electronics, Fuzzy is proven to be unavoidable in

increasing equipment efficiency and diagnosing malfunctions (Aptronix Inc., 1996).

According to Ilyas and Yunis (2006) the changes brought to the production methods and process

as a result of modernization requires a great flexibility and fast reaction in order to be able to cope

with the prevailing challenges. Such challenges trigger the non-linear system behaviour of partly

unknown systems, the conventional control methods does not offer good performance. The

introduction of fuzzy logic (FL) control has come to play an important role in meeting the

requirements to be able to face the new challenges. This is achieved by applying a strategy that

supports simplicity of design, based on linguistic information. This makes fuzzy logic (FL)

control method to outperform the conventional PID control method most especially in the

nonlinear industrial systems.

The technical control of fuzzy logic was applied to improve the efficiency of energy balance of a

dimmer light implemented in passive optical fiber daylighting system. In this work, Fuzzy logic

(FL) was used as measurement of intensity and occupancy in a room to control both parameters to

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accomplish a constant level in illuminance in the room. The result is obtained as the energy

power savings, this establishes the benefits and value of sunlight in addition to a clean,

environment friendly day lighting system. (Sulaiman et al., 2005).

The process of mixing plays a pivotal role in the production of beverages to determine the rate of

output of production. Therefore, the inputs proper control it is necessary to be embedded in such a

system. The mixture rate of the concentration has been indiscriminate in nature and unstable in

the form of a wave, Therefore, a fuzzy logic control is necessary for developing and

implementing an effective and efficient mixing process in factory production company bottling

(Nigerian Bottling Company Plc as a case study), so that a stable and optimal mixing process is

guaranteed (Philip, 2007).

According to (Chuen, 1990) the lack of quantitative data regarding relations of inputs and

outputs make the world of industrial processes to become uncontrollable by the conventional

control methods. The applications of fuzzy set theory in fuzzy control have attracted several

research interests in these applications. The fuzzy logic controller (FLC) based on fuzzy logic

offers the possibility of translating a linguistic control strategy based on expert knowledge into an

automatic control strategy.

According to Brackney and Shoureshi (2000) in their article titled “Fuzzy-Based Self-Organizing

Control for Building Systems”, a self-organizing controller based on fuzzy set theory is applied to

the problem of building temperature and lighting control. Fuzzy sets offering area occupancy,

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facility cost, and building dynamics are used as inputs to an adaptive fuzzy relation algorithm that

generates local controller set points.

It was gathered that, fifty percent energy savings was presented with automatic lighting controls

which applied at an office building in the San Francisco Bay Area. This system was used to apply

the energy savings and require reduction capabilities lighting control system designed to exploit

all the main control strategies, including scheduling, daylighting, and cavity maintenance. In the

work, it was indicated that, appropriate lighting controls may represent one of the most effective

methods of minimizing in energy use to achieve reduced electricity requirement in buildings

(Francis et al. 1993).

According to Mohd. et al. (2003) the development of intelligent lighting management system

outgrowing nowadays. The main objective is to make lighting controls easily accessible for

residents so as to achieve an optimized lighting design. In the project, ts lighting control was

selected since light serves as the backbone of an household electrical appliances. With the system,

all the lights in different places within a household can be controlled from one main switch panel.

His makes control and monitoring easier, mostly done by only one person at a particular location

where the switch panel is installed. Designing lighting management system of a smart house has

been successfully achieved.

Ziqiang et al. (2000) developed a closed loop control system incorporating fuzzy logic for solving

a class of industrial temperature control problems. A unique fuzzy logic controller (FCL)

structure with an efficient realization and a small rule base that can be easily implemented in

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existing industrial controllers was proposed. It was subjected to both software and hardware test

within an industrial setting that, the fuzzy logic control is much more capable that the current

temperature controllers. This statement has been confirmed with the following achievements of

fuzzy logic controller:

• It compensates for mass changes in the system

• It deals with unknown variables

• It operates at a very different temperature without re-tuning.

This is achieved by constructing in FLC, a classical control strategy, an adaption mechanism to

compensate for the dynamic changes in the system. The proposed FLC was applied to two

different temperature processes and significant improvements in the system performance were

observed in both cases. Furthermore, the stability of FLC is investigated and a safeguard is

established. This research focuses mainly on fuzzy logic control as an alternative strategy to the

current proportional integral-derivative (PID) method used widely in the industry.

The temperature is measured by suitable sensory equipment such as Thermocouples, Resistive

Thermal Devices (RTD’s), Thermistor, or other suitable electronic devices. The signal produced

by these devices is converted into a signal acceptable to the controller. The controller compares

the temperature signal with the desired temperature standard and actuates the control element.

The control element then alters the manipulated variable to change the quantity of heat being

added or taken from the process. The controller is purposely to regulate the temperature as close

as possible to the set point. To test the new fuzzy logic control algorithms, two temperature

regulation processes were used in this research. One used hot and cold water as moderating

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variable and as the controller element, the other uses electricity as a power source to a heater,

actuated by a Solid State relay (SSR). The new algorithms were tested extensively in both the

simulation and the hardware test.

According to Cziker et al. (2007) the concept of continual development has attracted a lot of

interests since lighting as any daylight concept has been very promising energy-dimming

opportunities; depending on some specialists, the possibility of achieving energy savings could

exceed 40%. On the other hand, daylight is a dynamic source of lighting, and the sky’s luminance

is not constant, and the changes in daylight can be quite noticeable depending on season, place,

latitude, or cloudiness. The lighting control systems are expected to adjust the lighting systems

when necessary in order to conform to the changing lighting conditions. Based on continuous

dimming the conventional control systems depict several difficulties to adapt their performances

to the quick changes in daylight and to preferences of occupants. Fuzzy control systems have

been adopted universally alternative to conventional control systems, it has been implemented in

the day lighting control systems.

Antonio and Jacobo (2000) designed and developed equipment that permits the development and

implementation of fuzzy control applications which are tested on modern home application, water

temperature and flow control. Achieving impressive characteristics of, firstly, compact, strong

and easy to use fuzzy application development equipment. Secondly the characteristic of reduced

hardware cost by utilizing many components which are recovered from old PCs. Thirdly the

computational methods uses table as well as integer calculation, achieving a reasonably speed

with low memory. Fourthly the characteristic of flexibility as the equipment can be used as a data

acquisition system or as any other (non-fuzzy) control application development system. It worked

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on both slave (computer attached) and stand-alone mode. Finally, the code is written in assembler

so it is optimized.

2.3 CONCLUSION

. Fuzzy Logic controller (FLC) has proved to be more suitable than the conventional control

algorithms such as PID controllers in terms of flexibility and fast reactions, simplicity in the

design, and good performance. Embedded control Fuzzy Logic (FL) is an example for an

alternative design methodology that can be performed to the development of both linear and non-

linear systems. Conventional and a Fuzzy approach have been used to develop a controller.

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CHAPTER 3

METHODOLOGY

This chapter describes the discussion states well the developing of fuzzy logic system and its

developing followed by the methodology used in the study the circuit design and the electronic

components. This section is designed to be part of automated controller lighting to control the

light intensity according to the number of the persons who entered or left the room. The

electronic circuits are defined in three circuits they are Infrared Transmitter, Infrared Receiver,

and PIC microcontroller Circuit, detail descriptions of each circuit and component are described

in this chapter.

3.1 Introduction

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Embedded control Fuzzy Logic (FL) is an example for an alternative design methodology that

can be performed to the development of both linear and non-linear systems. Conventional and a

Fuzzy approach have been used to develop a controller, the sequence of design steps are exhibited

in Fig. 1(a), (b) (Joseph, 2006).

Understand Physical

System and Control

Develop a Linear

Model of Plant

Simulate, Debug, and

Implementation

Determine a Simplified

Controller from

Develop an algorithm

for the Controller

Understand Physical

System and Control

Design the Control

for Using Fuzzy Rules

Simulate, Debug, and

Implementation

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Figure 1 (a) Conventional Design Figure 1 (b) Fuzzy Logic Controller Design

In order to appreciate why a fuzzy based design methodology is very attractive in embedded

control applications let us examine a typical design flow. Figure 1 (a, b) illustrates a sequence of

design steps required to develop a controller using a conventional and a Fuzzy approach.

Using the conventional approach our first step is to understand the physical system and its control

requirements. Based on this understanding, our second step is to develop a model, which includes

the plant, sensors and actuators. The third step is to use linear control theory in order to determine

a simplified version of the controller, such as the parameters of a PID controller. The fourth step

is to develop an algorithm for the simplified controller. The last step is to simulate the design

including the effects of non-linearity, noise, and parameter variations. If the performance is not

satisfactory we need to modify our system modeling, re-design the controller, re-write the

algorithm and retry

Based on Sulaiman et al. (2006), the study of the controller includes several phases, they are:

fuzzy logic system, software development, integration of software and microcontroller hardware,

hardware circuit development, and interfacing the microcontroller with the hardware that includes

the testing and evaluation of the system. Detail descriptions of each phase are shown in Fig. 3.2.

Start

Hardware Circuit Development

Integration of Software

Fuzzy Logic System

Software Development

Interface Microcontroller and

Test the System Design

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Figure 3.2: The flowchart of the work flow

3.2 Fuzzy Logic System

Fuzzy logic system is a system recognition algorithm for fuzzy control (Cristina et al., 2002). In

general, the main structures in FL control system are divided into three categories: fuzzification,

inference engine, and defuzzification as shown in Fig. 3.3.

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Figure 3.3: Components of a Fuzzy System.

The integration of fuzzy logic and lighting control to improve the light intensity and for flexibility

and fast reaction, FL control can play an important role and can easily be implemented in systems

with unknown structure. The generic structures of an automated light controller using fuzzy logic

are represented in Fig. 3.4.

Figure 3.4: The generic structures of an automated light controller using fuzzy logic

The fuzzification is the first stage in FL system computation to translate the real world variables

into fuzzy sets; otherwise it is to transform the entered numeric values into fuzzy sets. In this

stage, fuzzification used process to convert or control input variables that come from the sensor

Fuzzification Fuzzy

Inference

Hardware

Design

Defuzzification Input

Light-Intensity

No. of people No. of

lamps

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into fuzzy sets. It is a necessary planning among the inputs to set the values of membership of

each fuzzy variable. Furthermore, fuzzification makes input variables in the primacy of the rule

coincident with fuzzy set presentation.

Fuzzy logic inference engines use a rule based expert system paradigm to derive new sets of

fuzzy linguistic variables (Fuzzy IF-THEN Rules/ Fuzzy Variables/Linguistic Variables). The

inference engine depicts a set of IF-THEN rules as a fuzzy expert system to represent the

relations between fuzzy sets and to derive the changes that occur in an input sensor. The inference

engine process has two steps that are aggregation and composition. Aggregation is a process for

computing the values of the IF part of the rules, while composition is a process for computing the

values of the THEN part of the rules (Philip, 2007).

The last phase in this methodology is the defuzzification. It is the procedure that translates the

fuzzy output from the inference engine into discrete or crisp output value. In general, the fuzzy

expert system for this step is the defuzzification of fuzzy set (linguistic variables) of the output

value of fuzzy sets into crisp or numeric values. After the fuzzy logic controller evaluates inputs

and applies them to the rule base, it must generate a usable output to the system it is controlling.

This may mean setting a voltage or current to particular value to control the light intensity of a

room as the objective of this project, or it may mean defining the energy efficiency of a dimmer

light balance as it nears its target. The fuzzy logic controller must convert its internal fuzzy output

variables into crisp values that can actually be used by the controller system. Can perform this

portion of the fuzzy control algorithm, known as defuzzification, in several ways. Two of the

most common methods are:

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LPT cable

• Maximum defuzzification method.

• Centroid calculation defuzzification method.

3.3 Software Development

To control the electrical equipment, Assembly language has been used to program the

microcontroller. Beside that, all phases involved in developing fuzzy system was coded using

Visual Basic language.

The system structure of fuzzy logic is the fuzzy logic inference flow from the input to the output

variables. The translation from analog input into fuzzy values can be performed through

fuzzification process in the input interfaces. The fuzzy inference can occur in rule blocks, each

rule block has linguistic control rule and linguistic variables as outputs. They are converted again

into analog variables through defuzzificaton process in the output interfaces. Fig. 4.5, shows the

entire fuzzy system structure which contains rule blocks, input and output interfaces.

Fuzzy

program

Control System

Fuzzy logic

System

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Figure 3. 5: The Structure of a Fuzzy and Control System.

The linguistic variables may not have values of numbers but so called “linguistic terms”. Tow

input variables are defined the study that are number of people to count the number of people and

light intensity to define the light intensity.

3.3.1 Fuzzification

For automated light control using fuzzy logic, two input variables have been identified, namely

No. of people and Light Intensity as shown in Fig. 3. 6

Figure 3. 6: Basic Fuzzy Logic Design for Automated Light Controller using Fuzzy Logic

Automated Light Controller

using Fuzzy Logic

Light Intensity

No. of people

Light Intensity

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Input variable “Number of people”, this input variable is a change of interrupts of user to

determine the number of people; as well us counting number of people in the room very

accurately. When somebody enters into the room then the counter is incremented by one and the

lamps in the room will incremented by one and when any one leaves the room then the counter is

decremented by one and lamps will decremented by one, this input presents three variables {few,

many, too-many}. The membership function graph of number of people is shown in Fig. 3.

5.The input variables “Light Intensity”, this input defines the case of lighting room depending on

the number of lamps, this input presents three variables {low, bright, very-bright}. The

membership function graph of light intensity is shown in Fig.3.7.

The first step in the method of calculating the fuzzy logic is to determine the numerical input

values of the membership in relation to the linguistic descriptors. Figures. 3. 7 and 3.8 illustrate

the membership functions and map in graphical form the determination of the membership values

of the numerical input values with relate to the evaluation terms of the expert. The No. of people

of 4 has a membership of 0.5 with relate to the term "Many". The Light Intensity of 2has a

membership of 0.5 with relate to the term "Bright".

µ Many Too-Many

3 5 7 9 No. of people

Few

4

0.5

1

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Figure 3. 7: Membership Function of No. of people (Variable 1)

µ

Figure 3.8: Membership Function of Light Intensity (Variable 2)

3.3.2 Fuzzy Inference

The next step of the calculation in the Fuzzy system is to combine (two descriptions) the

evaluations of the input factors. To find cases that belong to one of the terms " Dim", "Bright"

0.5

2

Bright V-Bright Dim

1 3 5 7 Light intensity

1

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and "V-Bright”. The expert system rules have been used to combine the of possible factors with

positive grades of truth, based on Fuzzy Associative Memory Table (Sulaiman, 2006). Table 3. 1

is the rule blocks that include the control strategy of the fuzzy logic system. Each rule block

confines all rules for the same context. A context is defined by the same input and output

variables of the rules. As example when the light intensity is “Bright” and the number of people is

“Too-Many” then the light intensity will be “Very-Bright”.

Table 3.1: Rule Block (FAM Table)

To determine the mapping of output variables to their corresponding output membership

functions, the weighted input membership function and corresponding rule base determine the

relative membership in the output function. After sensors sense the input values and using the

first step in fuzzy logic system inputs are fuzzified and then by implementing the fuzzy rules if-

else the output fuzzy function obtained and using the current value the output for light intensity is

obtained. The output variables that identifies to the input variables as shown in Table 3.2

No. of people Light-Intensity

Dim Bright V-Bright

Few Dim Dim Dim

Many Bright Bright Bright

Too-Many Very-Bright Very-Bright Very-Bright

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Table. 3.2: Light Intensity (Membership Function Relative Membership)

3.3.3 Defuzzification

After fuzzified the input factors and combined the evaluated variables result, the last stage is

Defuzzification process. Defuzzification is aprocess to translate the Fuzzy output set into a single

numerical output value. The linguistic terms of a factor could take the positive values of the

membership. The highest membership value with relate to each of the terms is determined for the

Input Variable Defining Rule Base Output Variable

No. of people (many) = 5

Light Intensity = Low

if no. of people = many & light

intensity = low then set no. of

lamps to 4

No. of lamps = 4

No. of people (low) = 1

Light Intensity = bright

if no. of people = low & light

intensity = bright then set no.

of lamps to 2

No. of lamps = 2

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numerical values of the output factor. The most common methods of calculation in defuzzication

is the centroid calculation defuzzification method, where the balance point from the chart below

computed as the representative value of the total value of the area. The exact numerical output

value is the value of X coordinates of the point of this balance. Fig.3.8 illustrates the fuzzy output

set of the light intensity factor, the centre of area from the graph below and numerical output

value of 2.28.

µ

Figure 3.9: The output of Defuzzification

The algorithm of defuzzification of the system is depicted in Fig. 3. 9

0.5

2.28

Bright V-Bright Dim

1 3 5 7 Light intensity

1

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Figure 3.10: The Defuzzification Code

3.4 INTEGRATION OF SOFTWARE

To integrate the software with hardware, the input32.Dll has SELECTED to do this task. The

advantage of Inpout32.dl is, it can work with all the windows versions and it uses to contact with

all the programs that use the printer port to control the ports where installs in

C:\WINDOWS\system32\Inpout32.dll. The following code has been used to call the input32.dll

in the program which has been written by Visual Basic 6:

Public Declare Function Inp Lib “inpout32.dll” _

Alias “Inp32” (ByVal PortAddress As Integer) As Integer

Public Declare Sub Out Lib “inpout32.dll” _

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Hardware Design

Fuzzy Fuzzy

Rule

Table

Output

Membership function

Alias “Out32” (ByVal PortAddress As Integer, ByVal Value As Integer)

Figure 3.11: The integrated FL and Hardware for the Automated Light

The flow of integrated system is depicted in Fig. 3. 12

\

Start

S1=0? T1=T1+1

Yes

T1=0? Ls = 0

Yes

No

T1=1 to Yes

All Ls = 0

S2=0?

No

T2=T2-1

Yes

Yes

Input

Membership function

Fuzzy Logic system

Output

Knowledge Base

Data base Rule base

DefuzzificatioInference

Engine Fuzzification

Sensors Actuator

No. of people

Light intensity

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Note:

Ls – All lamps, L1 – lamp1

T – Timer, T1 – Timer1, T2 – Timer2

S – Sensor, S1 – Sensor1, S2 – Sensor2

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Figure 3. 12: The flow of integrated system

For completeness, the fuzzy logic light controller and the circuit design are presented in the

following subsection:

3.4.1 Fuzzy Logic Controller Design

The Fuzzy System of Automated Light controller receives the input from the sensors and

implemented the fuzzy control rules and actions. The general fuzzy logic control design is shown

in Fig.3.11.

Figure 3.13: Fuzzy Logic Control Design

The sensor receives its input from the surrounding environment when new interrupt happened and

sends its output to fuzzy control system. As a result, after fuzzy processing, the appropriate value

to environment will be sent to make the new action as shown in Fig.3.12.

Fuzzy System

Input Output/action

Fuzzy Process

Input Sensors

Input

Actions Appropriate

value

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Figure 3.14: Detail FLC System

The Fuzzy Logic Control system for this study consists of 2 inputs: No. of people, and light

intensity. The appropriate output created by the Fuzzy Logic Control comprises of appropriate

No. of lamps, and appropriate light level (light intensity) as shown in Fig. 3.14.

Figure 3.15: Context Diagram

3.4.2 System Design

The system design consists a number of lamps control to define the light intensity (light

level). The following subsection illustrated the design phase.

� Lamps control system

Fuzzy Logic

Automated light controller

Sensor detected

Lamps fuzzy system

Hardware system

People in

People out

1.1

1.2

1.3

Amount of people in room

Appropriate no. of lamps

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Figure 3.16: Lamps control system

(1.1) No. of people in/out system

• people in system

Figure 3.17: No. of people in system

• people out of system

Figure 3.18: No. of people out of system

Timer on/add 1 person

Lamps fuzzy system

People in

1

2

Amount of people in room

Timer off/minus 1 person

Lamps fuzzy system

People out

1

2

Amount of people in room

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(1.2) Lamps fuzzy system

Figure 3.19: Lamps fuzzy system

Light intensity control system

Figure 3.20: Light control system

Lamps Fuzzy System

Hardware System

Amount people in room

1

2

Appropriate lamps number

Sensor detected

Light fuzzy system

Hardware system

People in

People out

2.1

2.2

2.3

Amount of people in room

Appropriate no. of lamps

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(2.1) No. of people in/out system

• People in system

Fig.ure 3.21: No. of people in system

• People out of system

Timer on/add 1 person

Light fuzzy system

People in

1

2

Amount of people in room

Timer off/minus 1 person

Light fuzzy system

People out

1

2

Amount of people in room

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Figure 3.22: No. of people out of system

(2.2) Light fuzzy system

Figure 3.23: Light fuzzy system

3.4.3 Circuit Design

Circuit design consists of microcontroller circuit design and transmitter and receiver

circuits design. The details of the circuits have been discussed in chapter 3.

3.4.3.1 Microcontroller AT89C52

The microcontroller AT89C52, which used in this project is discussed further since it is

the main control system connected to the computer and hardware. It receives its input

from Fuzzy system to control the number of lamps depending on its programming which

Light Fuzzy System

Hardware System

Amount people in room

1

2

Appropriate lamps number

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relate with the output of the fuzzy system. These inputs are a 5 or 0 volt considered by

the microcontroller as 1 or 0, which means binary value.

� Microcontroller system

Figure 3.24: Microcontroller System

(1.1) No. of people in/out system

•••• People in system

Figure 3.25: No. of people in system (microcontroller)

Fuzzy system

Microcontroller system

Lamps

Sensor output

1.1

1.2

1.3

Control lamps

Appropriate no. of lamps

Microcontroller

Lamps

People in

1

2

Control lamps/ add 1 lamp

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•••• People out of system

Figure 3.26: No. of people out of system (microcontroller)

3.4.3.2 Transmitter and Receiver Circuits (TX & RX)

The details of both circuits have been discussed in chapter 3. The transmitter circuit is beamed

across the doorway and the receiver circuit in the other side of the doorway. When the user walks

through the doorway that will be triggered and the changeable of event will be happened in

receiver circuit. Furthermore, there are two transmitter and receiver circuit which presented in

this chapter as A and B. Transmitter circuit A connected with receiver circuit A for determine the

number of people which entered the room, and the transmitter circuit B connected with receiver

circuit B for determine the number of people which left the room.

� TX and RX circuits A System

Microcontroller

Lamps

People out

1

2

Control lamps/minus 1 lamp

Receiver circuit A

1.1

1.2

Infrared signal

Transmitter circuit A

1.3

New action / interrupt

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Figure 3.27: TX and RX circuit A System

(1.1) TX and RX circuits A (people in)

•••• TX circuit A

Figure.3.28: TX circuit A

•••• RX circuit A

Figure 3.29: RX circuit A

Receiver circuit A

Fuzzy System

Infrared signal

1

2

Appropriate new action

Receiver circuit A

2

Infrared signal

Transmitter circuit A

1

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(1.2) TX and RX circuits A Fuzzy System

Figure 3.30: TX and RX circuits A Fuzzy System

� TX and RX circuits B System

Figure 3.31: TX and RX circuit B System

(1.1) TX and RX circuits B (people out of)

Fuzzy System

Hardware System

New action

1

2

Appropriate new action

Receiver circuit B

Fuzzy System

2.1

2.2

Infrared signal

Transmitter circuit B

2.3

New action / interrupt

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•••• TX circuit B

Figure 3.32: TX circuit B

•••• RX circuit B

Figure 3.33: RX circuit B

(2.1) TX and RX circuits B Fuzzy System

Figure 3.34: TX and RX circuits B Fuzzy System

Receiver circuit B

Fuzzy System

Infrared signal

1

2

Appropriate new action

Fuzzy System

Hardware System

New action

1

2

Appropriate new action

Receiver circuit B

2

Infrared signal

Transmitter circuit B

1

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3.5 Hardware Circuit Development

Figure 8 shows the block diagram of fuzzy controlling lighting System. The number of people

and Light Intensity sensor communicated with microcontroller AT89C52. The sensors 1,3

defined as number of people sensor and the sensors 2,4 defined as Light Intensity sensors. The

output from the microcontroller AT89C52 will be depending on the lamps which used in this

study. The microcontroller is responsible for control how many lamps will be on or off according

to its programming. The transmitter circuit 1 will send a signal by infrared sensor to receiver

circuit 1 to increase number of the bright lamps when someone cut this signal in case somebody

is entering the room. The transmitter circuit 2 will send a signal by infrared sensor to receiver

circuit 1 to decrease number of the bright lamps when someone cut this signal in the case of

exiting from the room. The receiver circuits 1,2 send the new cases of entering or exiting of the

room to control the light intensity by microcontroller as shown in Fig.3. 8.

Sensor3 Sensor1

Transmi/er Circuit 2 Receiver Circuit 2

Sensor2 Sensor4

Transmi/er Circuit 1 Receiver Circuit 1

Relay2

Relay1

AT89C52

ATMEL

Relay2

Relay2

Lamp1

Lamp2

Lamp3

Lamp4

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Figure 3.35: Block Diagram of Fuzzy Control Lighting System

3.5.1 Hardware Implementation

The hardware architecture of this project consists of six major components, personal computer,

parallel port cable (LPT), infrared sensors, transmitter and receiver electronic circuits, relays, and

microcontroller ATMEL AT89C52. The hardware architecture is shown in Fig.3.12.

Figure 3.36: Hardware Architecture

3.5.1.1 Personal Computer

The Electronic Circuit

& Sensors

Microcontroller Personal

Computer

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The computer acts as brain of system. It runs the fuzzy logic program that has been done using

Visual Basic to make the right decision using information gathered from other components of the

architecture. The computer has been connected to hardware design using Parallel Port Cable

(LPT).

3.5.1.2 Parallel Port Cable (LPT)

In short, the printer port of a 25 Holes or Pins as a performing, they send 0 in the case Close and

1 in the case of Open, where 1 is a value approaching of 5 volte often. The numbers 1, 14, 16, 17

called control Pins used for the input and output at the same time. The numbers 2 to 9 called Data

Pins, they are the most common and the frequently used, in the printing, these outlets were used

to transfer data to the printing process in preparation for printing. The numbers 10 to 13, 14 called

Statues Pins these ports are used for entering data, and printers were used to enter data from the

printer to the computer, such as letters of notice (the papers finished). The numbers 18 to 25

called Ground Pins, like any electrical circuit, there must be a positive pole and negative pole to

serve the circuit, the Ground is a negative pole. The Fig.3.10 shows the structure of LPT:

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Figure 3.37: Structure of LPT

3.5.1.3 Infrared Sensor (Receiver & Transmitter)

The Infrared Sensor used as a source of infrared rays and it can detect these radiations. It has

transmitter and receiver feature is called as IR TX-RX pair. In this project the IR TX sends a

sequently signal to receive it by IR RX and when somebody interrupt this signal the IR RX will

give 0 output. The Fig. 3. 13 shows the infrared receiver and transmitter.

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Figure 3.38: Infrared Sensor (Transmitter & Receiver)

3.5.1.4 Transmitter Circuit

The transmitter circuit contains of the following components:

• IC 555

• Resistors

• Capacitors

• IR LED (sensor)

The IR infrared light is a sensor considers as the main component in both circuits “receiver and

transmitting”, it’s the responsible for making a new event when the interrupt happened by cutting

the IR signal, it uses to send its signal to the IR Receiver Circuit. The IC 555 is an integrating

circuit used to create a stable multivibrator which has two semi-stable states and to generate a

square signal; this signal is required to switch ‘ON’ the IR LED. The transistor BC 548 is used to

drive the IR LED. The IR transmitter circuit is shown in the Fig. 3. 14 below:

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Figure 3.39: IR Transmitter Circuit

3.5.1.5 Receiver Circuit

The receiver circuit contains of the following components:

• TSOP1738 (sensor)

• IC 555

• Resistors

• Capacitors

The second step in the whole circuit after transmitting the signal which has been sent by IR

Transmitter Circuit is receiving this signal by Receiver Circuit. The changes of the new event

(enter or leave) will be in this section. After the interrupt happened the output of IR sensor

momentarily will be a low state (0 volt). The IC 555 receives the output of the IR sensor to create

a short pulse and apply this pulse to the microcontroller which we will describe it later. The IR

receiver circuit is shown in the Fig. 3. 15

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Figure 3.40: IR Receiver Circuit

3.5.1.6 Microcontroller Circuit

In this circuit the microcontroller AT89C52 is the final result of the hardware design. After

receiving the signal from IR Transmitter circuit through IR Receiver circuit the output of the

second circuit passes into the input of microcontroller circuit. The aim is to control how many

lamps will be “ON” according to the number of the interrupts when someone cuts the signal. To

control the number of the lamps, microcontroller has the ability to programming by C language or

Assembly language. For this project C language has been used to program the microcontroller.

The microcontroller with circuit diagram are shown below in Fig. 3. 15 and 3. 16

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Figure 3.41: Microcontroller Circuit Diagram

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Figure 3.42: The whole Circuit Diagram

3.6 INTERFACE MICROCONTROLLER and TEST the SYSYSTEM DESIGN

Several experiments were conducted to investigate the behaviour of micro controller that

connected to fuzzy system to control the number of lamps depending on the fuzzy system output.

The design and testing of the hardware circuit were conducted on experimental circuits and

several diodes were conducted to investigate the output of hardware circuit. The circuit interface

was divided into several circuits as following:

3.6.1 Transmitter Circuit Interface

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Figure .3.43: Transmitter Circuit Interface

The transmitter circuit uses two infrared sender sensors for sending infrared to receiver circuit.

The circuit design voltage (12 VDC), to turn circuit on.

3.6.2 Receiver Circuit Interface

Figure 3.44: Receiver Circuit Interface

The receiver circuit uses infrared receiver sensor to get infrared from transmission. It has two

LEDs display when circuit is on and when sensor sense signal sent.

3.6.3 Receiver and Transmitter Circuits Interface

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Figure 3.45: Receiver and Transmitter Circuits Interface

The above figure shows the receiver and transmitter circuit when they connected to each other.

The receiver circuit 1 illustrates that turned on and received infrared signal while the receiver

circuit 2 only turned on.

3.6.4 Microcontroller Circuit Interface

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Figure 3. 46: Microcontroller Circuit Interface

Microcontroller is the brain of hardware design. Microcontroller receives output data respectively

from fuzzy system. This data is 5 or 0 volt represents to microcontroller as 1 or 0 data and

converts this data into purpose output according to its program.

3.6.5 The Circuit of Automated Light Controller using Fuzzy Logic Interface

This project has two general parts: computer and hardware circuit. The computer is the brain of

the whole system while the microcontroller is the brain hardware circuit. The PC acts as a central

station of communication between hardware circuit and fuzzy system. The PC carries the data

from hardware circuit to fuzzy system and after fuzzy process it carries the fuzzy data to

hardware circuit. Fig.3. 22 shows the whole circuit interface for this project.

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Figure 3. 47: The Circuit of Automated Light Controller using Fuzzy Logic Interface

CAHPTER 4

RESULTS AND DISCUSSION

This chapter discusses the experimental results of the software and hardware circuits. The

results were obtained during the system testing phase, based on fuzzification, fuzzy

inference and defuzzificati. On the test was implemented on the hardware part as an

input variable to obtain the response from software. The result obtained from the

integrated circuit is also reported.

4.1 FUZZIFICATION

In this study, two inputs are integrated with the fuzzy system. That are no. of people and

light intensity. Both inputs the real values have been implemented in fuzzification phase.

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The fuzzification converted these crisps inputs into linguistic variables based on the

membership function to obtain the confidence values. The numbers of confidence values

are generated depending on the number of linguistic variables. For this study, there are

three linguistic quantifier for each fuzzy variable. At the same time there are three

confidence values that have been generated. Listed below are the fuzzy variables defined

using linguistic quantifiers.

No. of people (NP) € {Few, Many, Too-Many}

Light intensity (LI) € {Dim, Bright, Very-Bright)

The membership function for each input fuzzy variables is shown in Fig.4.1 and Fig. 4.2.

Figure 4.1: Membership Function graph for No. of People

0.5

2

1

Bright V-Bright Dim

1 3 5 7 Light intensity 0

0

4

0.5

1

Many Too-Many

3 5 7 9 No. of people

Few

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Figure 4.2: Membership Function graph for Light intensity

The confidence value which is obtained by fuzzification step is accepted in the next step

of fuzzy logic system. The code for fuzzification code and the process of getting the

confidence value for fuzzy variables is presented in Fig.4.3.

Figure 4.3: The Fuzzification Phase

For testing, four different cases have been chosen to produce difference output for each

step in fuzzy system and the output of hardware design. Refining to Fig. 4.1 and Fig. 4.2,

when no. of people = 3 the confidence value for its linguistic descriptors are {1,0,0}. If

the light intensity = 2, the confidence value for its linguistic descriptors are {0.5,0.5,0}.

For the first case the no. of people is “Few” and the light intensity is “Dim” as shown in

Fig. 4.6.

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Fig. 4.7 and Fig. 4.8. represent the membership function of the first case

which No. of people = 3 and light intensity = 2.

Figure 4.5: Membership Function graph for NP=3

Figure 4.6: Membership Function graph for LI=2

Figure 4.4: Fuzzification Test1: NP=3,

0 4

0.5

1

Many Too-Many

3 5 7 9 No. of people

Few

0.5

2

1

Bright V-Bright Dim

1 3 5 7 Light intensity

0

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For the case the no. of people is set to “Few” with different in the expression of

membership function with first case and the light intensity is Dim as shown in Fig. 4.7.

Figure 4.7: Fuzzification Test2: NP = 4, LI = 2

To prove that the next membership functions for both of input present the secon case as shown in

Fig4.7 and Fig. 4.8.

0 4

0.5

1

Many Too-Many

3 5 7 9 No. of people

Few

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Figure 4.8: Membership Function graph for NP=4

Figure 4.9: Membership Function graph for LI=2

Figure 4.10: Fuzzification Test3: NP = 4, LI = 4

To prove that the next membership functions for both of input present the first case as showing in

Fig. 4.7 and Fig. 4.8.

0.5

2

1

Bright V-Bright Dim

1 3 5 7 Light intensity

0

0 4

0.5

1

Many Too-Many

3 5 7 9 No. of people

Few

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Figure 4.11: Membership Function graph for NP=4

Figure 4.12: Membership Function graph for LI=4

Figure .4.13: Fuzzification Test4: NP = 6, LI = 6

To prove that the next membership functions for both of input present the first case as showing in

Fig4.7 and Fig.4.8.

0 4

0.5

1

Many Too-Many

3 5 7 9 No. of people

Few

6

0.5

2

1

Bright V-Bright Dim

0

1 3 5 7 Light intensity

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Figure 4.14: Membership Function graph for NP=6

Figure 4.15: Membership Function graph for LI=6

4.2 FUZZY INFERENCE

Fuzzy inference presents IF-THEN rules which implemented on the fuzzy sets of the

inputs; no. of people and light intensity by choosing the minimize confidence values for

the row and column as resultant confidence value. Depending on the first case, the first

confidence value for the first column “Dim” will compared with the first confidence

value for the first row “Few”. The minimum value between these two values will be

taken as the value which appropriate to the output. FAM Table of no. of people and light

intensity is shown in Table.4.1.

Table 4.1: FAM Table

No. of people Light-Intensity

Dim Bright V-Bright

Few Dim Dim Dim

6

0.5

2

1

Bright V-Bright Dim

0

1 3 5 7 Light intensity

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Based on the table above, the following IF-THEN rules have been generated. The

columns represent the linguistic variables for input light intensity, while the rows

represent the linguistic variables for input no. of people. Listed below are a nine rules:

Notes: No. of people = NP

Light intensity = LI

• IF NP is LOW AND LI is DIM THEN LI is DIM

• IF NP is LOW AND LI is BRIGHT THEN LI is DIM

• IF NP is LOW AND LI is V-BRIGHT THEN LI is DIM

• IF NP is MANY AND LI is DIM THEN LI is BRIGHT

• IF NP is MANY AND LI is BRIGHT THEN LI is BRIGHT

• IF NP is MANY AND LI is V-BRIGHT THEN LI is BRIGHT

• IF NP is TOO-MANY AND LI is DIM THEN LI is V-BRIGHT

• IF NP is TOO-MANY AND LI is BRIGHT THEN LI is V-BRIGHT

• IF NP is TOO-MANY AND LI is V-BRIGHT THEN LI is V-BRIGHT

The algorithm for FAM Table and the process of getting the minimum value of the

implementation IF-THEN rules on the linguistic variables are presented in Fig. 4.16.

Many Bright Bright Bright

Too-Many Very-Bright Very-Bright Very-Bright

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Figure 4.16: FAM Table Algorithm

The functionality of fuzzy inference is compared with the confidence values for the input

variables and chose the minimum value by mapped IF-TEHEN rules as following FAM

Table in Fig.4.17.

Figure 4.17: FAM Table Test1: NP=3, LI=2

The minimum values have been chosen depending on the following IF-THEN rules. The

minimum value is determined by comparing the first column wit first row as following

rules:

Note: No. of people is NP, Light intensity is LI.

IF NP < LI THEN MinVal = NP NP = 1 , LI = 0.5

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Else THEN

MinVal = LI MinVal = 0.5

For the second case no. of people = 4 and light intensity = 2. The details of counting

minimum value have been discussed in first case. FAM table is represented as shown in

Fig. 4.18.

Figure 4.18: FAM Table Test2: NP=4, LI=2

For the third case no. of people = 4 and light intensity = 4. The details of counting

minimum value have been discussed in first case. FAM table is represented as shown in

Fig. 4.19.

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Figure 4.19: FAM Table Test3: NP=4, LI=4

For the fourth case no. of people = 4 and light intensity = 4. The details of counting

minimum value have been discussed in first case. FAM table is represented as shown in

Fig.4.20.

Figure 4.20: FAM Table Test4: NP=6, LI=6

4.3 Defuzzification

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The final result of the fuzzy system is to convert the confidence values back to real values

that can be appropriated to the user input. The method of center of area (COA) has been

used to determine the real value of the output. Defuzzification is the last step in fuzzy

system to map the output depending on fuzzy process. It sent its result to the hardware

design to control the number of lamps according to the fuzzy process output. The

algorithm for defuzzification and the process of getting the real values which appropriate

to user inputs is presented in Fig. 4.21.

Figure 4.21: The Defuzzification Phase

The defuzzification used the method of center of area (COA) as discussed in another

section to find out the output for defuzzification step. The algorithm for this

defuzzification method is:

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To prove that the next membership function for both of inputs presents the first case is

no. of people = 3 and light intensity = 2 as showing in Fig. 4.22.

Figure 4.22: Defuzzification Membership Function Test1

Fig. 4.23. shows the software testing of defuzzificatin step of the real value and the

output which mapped into hardware input.

Figure 4.23: Defuzzification Test1: LI = 2 = LOW

After defuzzified the confidence values converted into real value that can be understood

by the user. This real value is an input to hardware design exactly input of

microcontroller to control the output of the hardware. For first case the output of

defuzzification is 2 mapped to microcontroller which controlled the number of lamps into

2 lamps. The hardware testing for first step is shown in Fig. 4.24.

0.5

2

1

Bright V-Bright Dim

1 3 5 7 Light intensity

0

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Figure 4.24: Hardware Design Test1= 2lamps

For the second case the output of defuzzification is BRIGHT which means three lamps

should be on as shown in Fig. 4.25. For more details as explained as first case.

Figure 4.25: Defuzzification Test2: LI = BRIGHT

The output of the hardware circuit shows that three lamps turned on depending on the

output of defuzzification process for second case. Fig. 4.26. illustrates the final output of

hardware circuit for second case.

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Figure 4.26: Hardware Design Test2 = 3lamps

The third case of defuzzification presented output which is BRIGHT as same as the

second case. Note that, there is a different in the number of people which entered the

room. Fig. 4.27 shows the output of diffuzzification for this case.

Figure 4.27: Defuzzification Test3: LI = BRIGHT

The output of hardware circuit is three lamps for third case and presented as shown in

Fig. 4.28.

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Figure 4.28: Hardware Design Test3 = 3lamps

The implementation of fourth case shows that the light intensity is BRIGHT as same as

the third case but different in number of people and the number of lamps that turned on.

Fig. 4.29 shows the output of defuzzification which was BRIGHT and the lamps that

should be on are four.

Figure 4.29: Defuzzification Test4: LI = BRIGHT

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Depending on the last case and the output of defuzzification the output of the hardware

circuit is four lamps. Fig. 4.30 shows the output of hardware circuit.

Figure 4.28: Hardware Design Test3 = 4lamps

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CHAPTER 5

CONCLUSION AND RECOMENATION

5.1 Conclusion

In this study, the practical implementation of a fuzzy logic controller for automated

lighting counting was presented. The automated light controller using fuzzy logic proved

to be effective system. It was demonstrated that the fuzzy logic control design method

resulted in better energy saving than conventional control method. The fuzzy logic

controller is a good responsive, practical and simple and gathers all requirements with the

features of a knowledge based approach with unknown system structure.

This study has achieved its objective, which is to design a fuzzy logic system which

integrates with hardware to control light intensity in a room. This study illustrates that

fuzzy logic controlling method could be a suitable alternative method compared to

conventional controlling methods.

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5.2 Recommendation

This project used sensors to detect signal if somebody entered or left the room. This

signal will be lost if the wiring sensor that used is longer than 10 feet. As solution, the

circuit need amplifier to make the signal stronger. Furthermore, the computer must be

located nearby to the sensors and it does not have the ability for easy movement. In

recent years, the bluetooth techniques have been widely use in many modern industries.

For this project, the bluetooth technique could be alternative to overcome the wiring

sensors problems.

This project was developed by using Microsoft Visual Basic 6.0. In the future, other

advance programming languages such as Java and C# can be used to develop the system,

which may make it having more features, faster other than what have been presented in

this version. The proposed system besides that does not have the ability to store

information. When the system is restarted again all last data was lost. For future work,

database could be designed to store and interact with new data.

The development of smart lighting control system in building is outgrowing nowadays.

The system should be able to not only detect changes in one room, as it does now, but

also could be designed for more than one room to determine in which room the changes

were occurring.

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