19 fuzy logic and its application in air conditioners ( soft computing)

8/3/2019 19 Fuzy Logic and Its Application in Air Conditioners ( Soft Computing)

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FUZZY LOGICFUZZY LOGIC

ANDAND

ITS APPLICATION IN AIR-CONDITIONERITS APPLICATION IN AIR-CONDITIONER

((Under the theme of soft computing)

Presented by:Presented by:

K.T.N.M.GUPT L.SANTHOSH KUMAR K.RAJESH

CSE CSE CSE

ROLLNO:07A51A0547 ROLLNO:07A51A0556 ROLLNO:07A51A0540

[email protected] [email protected]@gmail.com
mailto:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]


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ABSTRACT:ABSTRACT:

The control

technology has been changing at

fast pace in the last few decades.

Artificial intelligence techniques

have received much attention in

almost every field of science and

engineering due to their

enormous potential. This paper

highlights the need of fuzzy

logic and its application in air

conditioner

Fuzzy Logic is an innovative

approach to help control

unpredictable systems with

accuracy. It provides a simple

way to arrive at a definite

conclusion based on vague,

ambiguous or missing input

information. It incorporates a

simple, rule based IF X AND

Y THEN Z approach in

solving a control problem.

Air conditioners use

the concept of fuzzy logic, in

which the room temperature is

compared with reference

temperature and an error

temperature is developed and its

value is defined by fuzzy sets.

Depending on this an

appropriate rule is applied and

heating or cooling valve is

adjusted to maintain room

temperature constant. Modified

air conditioner is also presented

where the humidity is also taken

into account. This paper also

presents some of the future

implementations of fuzzy logic.

This paper is helpful

in understanding some of thefundamentals of fuzzy logic.

CONTENTS

INTRODUCTION

WHAT IS FUZZY LOGIC? WHY FUZZY?

FUZZY LOGIC IN AIR-CONDITIONERS


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FUTURE APPLICATIONS

ADVANTAGES

DISADVANTAGES

CONCLUSION

INTRODUCTION:INTRODUCTION:

The control

technology has been changing at

fast pace in the last few decades.

The introduction of the latest

technology has been used to

only glamorize the controls but

deep inside it's the same old

algorithms where the user has to

feed the data. The user demands

more comfortable conditions

where the controls have to

understand the feelings of the

user, and to achieve this, these

controls will have to think like

humans (artificial intelligence).

For a control to be truly

intelligent, it must possess the

symbol-manipulating, rule-

implementing proficiency that

characterizes human reasoning.

Many scientists have

struggled for decades to develop

the concept of artificial

intelligence. At last, Simon

describes his first ARTIFICIAL

INTELLIGENCE program in

1955. This logic is based on

approximate reasoning and is

called FUZZY LOGIC. The

Fuzzy set, the basis of Fuzzy

Logic,was developed by

Professor Lotfi Zadeh in 1965.

WHAT IS FUZZY LOGIC?WHAT IS FUZZY LOGIC?


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Fuzzy logic is a

superset of Boolean logic

dealing with the concept of

partial truth. Fuzzy logic

replaces Boolean truth values

with degrees of truth which are

very similar to probabilities,

except they need not add up to

100%.


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The above fuzzy set

can be any shape triangular,

trapezoidal or a bell curve. The

triangle shape is used most often

today because it is easy to work

with.

Fuzzy logic can be

implemented in hardware,

software, or a combination of

both .It provides a simple way to

arrive at a definite conclusion

based on vague, ambiguous,

imprecise, noisy or missing

input information

FIGURE


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Fuzzy Logic is an

innovative approach to help

control unpredictable systems

with accuracy. It incorporates a

simple, rule based IF X AND Y

THEN Z approach to solving a

control problem rather than

attempting to model a system

mathematically. Fuzzy control,

which directly uses fuzzy rules,

is the most important application

in fuzzy theory.

The operation of a fuzzy expert

system depends on the execution of

FOUR major tasks


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FUZZIFICATIONFUZZIFICATION: It

involves the choice of

variables, fuzzy input

and output variables and

de justified output

variable(s), definition of

membership functions

for the input variables

and the description of

fuzzy rules. The


defined on the input

variables are applied to

their actual values to

determine the degree of

truth for each rule

premise.

INFERENCE:INFERENCE: The

truth-value for the

premise of each rule is

computed and the

conclusion applied to

each part of the rule.

Fuzzy Logic more

closely approximates human

thought process than standard

PID

(Proportional/Integral/Derivative

) control methods do. Since

some process control systems

are difficult to control with only

PID, the addition of Fuzzy Logic

provides an excellent solution.

The fuzzy logic analysis and

control method is given as

Receiving of one, or

large number of measurements.

Processing all these

inputs according to

human based, fuzzy "If-

Then" rules, which can

be expressed in plain

language words

Averaging and weighting

the resulting outputs

from all the individual

rules into one single

output decision or signal

which decides what to

do. The output signal

arrived is a defuzzified

value.

MIN and PRODUCT aretwo inference methods.

COMPOSITIONCOMPOSITION: All

the fuzzy subsets

assigned to each output

variable are combined

together to form a single

fuzzy subset for each

output variable. MAX

and SUM are two


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composition rules

DEFUZZIFICATIODEFUZZIFICATIO

NN: The fuzzy value

produced by the

composition stage needsto be converted to a

single number value.

Two popular

defuzzification

techniques are the

CENTROID and

MAXIMUM techniques

WHY FUZZY?WHY FUZZY?

In order to appreciate

why a fuzzy based design

methodology is very attractive in

embedded control applications,

we examine a typical design

flow. Figure illustrates a

sequence of design steps

required to develop a controller

using a conventional and a

Fuzzy approach.


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Using the conventional approach

First step is to

understand the physical

system and its controlrequirements.

Second step is to

develop a model which

includes the plant,

sensors and actuators.

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.

Fourth step is to

develop an algorithm for

the simplified controller.

Fifth step is to simulate

the design including the

effects of non-linearity,

noise, and parameter

variations.

Using fuzzy logic:Using fuzzy logic:

First step is to

understand and

characterize the system

behavior by using our

knowledge and

experience.

Second step is to

directly design the

control algorithm using

fuzzy rules.

Third step is to simulate

and debug the design. If

the performance is not

satisfactory we only need

to modify some fuzzy

rules and re-try.

Although the two

design methodologies are

similar, the fuzzy-based

methodology substantially

simplifies the design loop. This

results in some significant

benefits, such as reduced

development time, simpler

design and faster time to market:

FUZZY LOGIC IN AIR-CONDITIONERS:FUZZY LOGIC IN AIR-CONDITIONERS:


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An air conditioner is

an appliance designed to extract

heat from a humanly occupied

space air temperature using a

refrigeration cycle .Air

conditioning systems are

essential in most of our daily

lives. Our expectations of such

systems have been raised to

demand more than just

temperature control, and it is

increasingly desirable to apply

these systems in varying

situations. A comfortable and

safe environment is often

difficult to define and affected

by sometimes contradictory

factors. Fuzzy logic control

provides an effective and

economic approach to this

problem.

The rules that are used to control the air conditioner canbe expressed as a cross product:

CONTROL = TEMP SPEED

The rules governing the air conditioner are as follows:

RULE 1: IF TEMP is COLD THEN SPEED is MINIMAL

RULE 2: IF TEMP is COOL THEN SPEED is SLOW

RULE 3: IF TEMP is PLEASENT THEN SPEED is

MEDIUM

RULE 4: IF TEMP is WARM THEN SPEED is FAST

RULE 5: IF TEMP is HOT THEN SPEED is BLAST

Temperature control is

widely used in various

processes. Conventional

approach usually does not result

in satisfactory temperature

control. So we use fuzzy logic to

control temperature which

employs an integrated fuzzy

inference development

environment (FIDE)

FUZZY CONTROLLER FOR AIR FUZZY CONTROLLER FOR AIR


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CONDITIONING SYSTEM:CONDITIONING SYSTEM:

In, this we

concentrate on the input/output

variables of the fuzzy controller

for an air conditioning system,

here we do not provide FIU

(Fuzzy Inference Unit) source

code.

A Basic Model:A Basic Model:

Let us start with the

simplest air conditioning system,

which is shown in figure. The

only control target in this system

is temperature which can be

changed by two adjustment

valves.

Air Conditioning System: Basic Model

There is a sensor in

the room to monitor temperature

for feedback control, and there

are two control elements,

cooling valve and heating valve,

to adjust the air supply

temperature to the room

.

Figure 2 Fuzzy

Controller for Air Conditioning

System: Basic Model

Rules for this controller may be

formulated using statements

similar to:

If temperature is low

then open heating valve

greatly

Values such as low are defined by fuzzy sets


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(membership functions). We can

use the MF-edit function in

FIDE to define the fuzzy sets.

Generally, membership

functions of fuzzy sets take on a

triangular shape because they

are effective and easy to

manipulate.

A Modified Model:A Modified Model:

In the real world,

however, it is usually not

enough to manage an air

conditioning system with

temperature control only. We

need to control humidity as well.

A modified air conditioning

system is shown in Figure 3.

There are two sensors in this

system: one to monitor

temperature and one to monitor

humidity. There are three control

elements: cooling valve, heating

valve, and humidifying valve, to

adjust temperature and humidity

of the air supply.

Figure 3 Air Conditioning System: Modified Model

A fuzzy controller for

this modified air conditioning

system is shown inFigure 4. Thetwo inputs to the controller are

measured temperature and

humidity. The three outputs are

control signals to the three

valves.

Figure 4 Fuzzy Controller for Air Conditioning System:
http://www.aptronix.com/fuzzynet/applnote/air.htmhttp://www.aptronix.com/fuzzynet/applnote/air.htmhttp://www.aptronix.com/fuzzynet/applnote/air.htmhttp://www.aptronix.com/fuzzynet/applnote/air.htmhttp://www.aptronix.com/fuzzynet/applnote/air.htm


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Modified Model

Rules for this

controller can be formulated by

adding rules for humidity

control to those already

formulated for temperature

control in the basic model.

Additional rules must take the

interference between

temperature and humidity into

account. For example, in winter,

when we use heat to raisetemperature, humidity is usually

reduced. The air thus becomes

too dry. To address this

condition, a rule statement

similar to the following is

appropriate:

If temperature is low then open humidifying valve slightly

This rule acts as a

predictor of humidity (it leads

the humidity value) and is also

designed to prevent overshoot in

the output humidity curve. We

could have used the following

rule:

If humidity is low then open humidifying valve slightly

But it's action, if

acting as the only rule for low

humidity, will be late when low

humidity is already the case

FUTURE APPLICATIONS:FUTURE APPLICATIONS:

Some companies are

developing new products that

will incorporate existing fuzzy

logic technology in order to

update their aging product lines.

Here are a few companies who

have various degrees of interest

in fuzzy logic:

PANASONICPANASONIC

has used fuzzy logic

technology in developing

a Home Cleaning Robot.

Previously robots used to

learn the dimensions of

dusty carpet by traveling

around the edge of the

room. Then, it moves

back to its starting point

and vacuums until the


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carpet is clean, or its

batteries are depleted.

The robot will stop

vacuuming when it only

has enough power to

travel back to its

recharging stand. It will

repeat this pattern until it

finishes the room.

Because of the built-in

fuzzy logic technology,

the Home Cleaning

Robot can determine

how much suction power

and beater bar speed it

needs to complete each

job

UNITED TECHNOLOGIES CORPUNITED TECHNOLOGIES CORP.

envisions a "smart" air

conditioner that will learn to

regulate room temperature

according to your exact

specifications "Imagine an air

conditioner that starts

automatically ... the moment you

walk into the room. And what's

more, it recognizes you,

remembers your preference for

comfort and maintains the

room's temperature, humidity

and air flow to your liking."

Researchers are developing

fuzzy and other AI systems so

that, if more than one person is

in the room, the air conditioner

can regulate an environment

which pleases everybody.

ADVANTAGES:ADVANTAGES:

Fewer values, rules and decisions are required.

More observed variables can be evaluated

Fine tuning by changing the membership functions.

Fuzzy logic is flexible.

Fuzzy logic is tolerant of imprecise data.

DISADVANTAGES:DISADVANTAGES:

Though they are easier to design and faster to


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prototype than

conventional control

systems, fuzzy systems

require more simulation

and fine-tuning before

they are operated.

Saturation problems if


are not defined properly.

Limited explanatory

system.

CONCLUSION:CONCLUSION:

We conclude that for

an engineer, an ideal machine is

one in which human requests are

automatically interpreted and

responded by adjusting itself

appropriately to variations in the

environment. Fuzzy logic can

help make this ideal a reality and

makes the effort easier. Fuzzy

logic was conceived as a better

method for sorting and handlingdata and an excellent choice for

many control system

applications. It is very robust.

In this context, Fuzzy

logic is a problem-solving

control system methodology that

lends itself to implementation in

systems ranging from simple,

small, embedded micro-

controllers to large networked

multi-channel PC or

workstation-based data

acquisition and control systems.

Many countries have

capitalized on "big science"

space and defense applications.

On the contrary, if they have

invested on fuzzy logic,

appliance innovation would have

taken place at a faster rate.

Artificial intelligence systems

like Fuzzy Logic hold the key

for the future economic

development.


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19 fuzy logic and its application in air conditioners ( soft computing)

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