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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
membership functions
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
membership functions
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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