AUTOMATIC CONTROL OF AIRCONDITIONER USING FUZZY LOGIC

Authorised BySANTOSH BHARADWAJ REDDYEmail: help@matlabcodes.comEngineeringpapers.blogspot.com

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ABSTRACT:

This paper gives a short introduction

into Fuzzy Logic, presents an overview

on fuzzy controllers, and its applications.

According to L.A.Zadeh, fuzzy logic is a

superset of conventional (Boolean) logic

that has been extended to handle the

concept of partial truth -- truth values

between "completely true" and

"completely false". Fuzzy Logic has

been gaining increasing acceptance

during the past few years. There are over

two thousand commercially available

products using Fuzzy Logic, ranging

from air conditioners to high speed

trains. Nearly every application can

potentially realize some of the benefits

of Fuzzy Logic, such as performance,

simplicity, lower cost, and productivity.

An air conditioner temperature control

apparatus comprising: a remotely

controlled signal receiving unit for

sensing room temperature; and a control

unit for carrying out fuzzy logic by

inputting the detected room temperature

and the temperature error between

detected room temperature and sensed

room temperature and for compensating

the temperature error to linear-control an

operation frequency of the compressor

by inputting the compensated

temperature error.

WHAT IS FUZZY LOGIC?:--Fuzzy

logic is a powerful problem-solving

methodology with a myriad of

applications in embedded control

and information processing. It was

introduced by Dr. Lotfi Zadeh of

UC/Berkeley in the 1960's as a

means to model the uncertainty of

natural language. Zadeh says that

rather than regarding fuzzy theory

as a single theory, we should

regard the process of

``fuzzification'' as a methodology

to generalize ANY specific theory

from a crisp (discrete) to a

continuous (fuzzy) form .Thus

recently researchers have also

introduced "fuzzy calculus", "fuzzy

differential equations", and so

on.Fuzzy provides a remarkably

simple way to draw definite

conclusions from vague,

ambiguous or imprecise

information. In a sense, fuzzy logic

resembles human decision making

with its ability to work from

approximate data and find precise

solutions.Unlike classical logic

which requires a deep

understanding of a system, exact

equations, and precise numeric

values, Fuzzy logic incorporates an

alternative way of thinking, which

allows modeling complex systems

using a higher level of abstraction

originating from our knowledge

and experience. Fuzzy Logic allows

expressing this knowledge with

subjective concepts such as very

hot, bright red, and a long time

which are mapped into exact

numeric ranges.Fuzzy Logic has

been gaining increasing

acceptance during the past few

years. There are over two

thousand commercially available

products using Fuzzy Logic,

ranging from washing machines to

high speed trains. Nearly every

application can potentially realize

some of the benefits of Fuzzy

Logic, such as performance,

simplicity, lower cost, and

productivity. Fuzzy Logic has been

found to be very suitable for

embedded control applications.

Several manufacturers in the

automotive industry are using

fuzzy technology to improve

quality and reduce development

time. In aerospace, fuzzy enables

very complex real time problems

to be tackled using a simple

approach. In consumer electronics,

fuzzy improves time to market and

helps reduce costs. In

manufacturing, fuzzy is proven to

be invaluable in increasing

equipment efficiency and

diagnosing malfunctions

APPLICATIONS OF FUZZY

LOGIC

Fuzzy logic can be used to control

household appliances such as washing

machines (which sense load size and

detergent concentration and adjust their

wash cycles accordingly) and

refrigerators.A basic application might

characterize sub ranges of a continuous

variable. For instance, a temperature

measurement for anti-lock brakes might

have several separate membership

functions defining particular temperature

ranges needed to control the brakes

properly. Each function maps the same

temperature value to a truth value in the

0 to 1 range. These truth values can then

be used to determine how the brakes

should be controlled.

In this image, cold, warm, and hot are

functions mapping a temperature scale.

A point on that scale has three "truth

values" — one for each of the three

functions. For the particular temperature

shown, the three truth values could be

interpreted as describing the temperature

as, say, "fairly cold", "slightly warm",

and "not hot".A more sophisticated

practical example is the use of fuzzy

logic in high-performance error

correction to improve information

reception over a limited-bandwidth

communication link affected by data-

corrupting noise using turbo codes. The

front-end of a decoder produces a

likelihood measure for the value

intended by the sender (0 or 1) for each

bit in the data stream. The likelihood

measures might use a scale of 256 values

between extremes of "certainly 0" and

"certainly 1". Two decoders may analyze

the data in parallel, arriving at different

likelihood results for the values intended

by the sender. Each can then use as

additional data the other's likelihood

results, and repeats the process to

improve the results until consensus is

reached as to the most likely values.

AIR CONDITIONING

TEMPERATURE CONTROL

Temperature control is widely used in

various processes. These processes, no

matter if it is in a large industrial plant,

or in a home appliance, share several

unfavorable features. These include non-

linearity, interference, dead time, and

external disturbances, among others.

Conventional approaches usually do not

result in satisfactory temperature control.

In this Application Note we provide

examples of fuzzy logic used to control

temperature in several different

situations. These examples are

developed using FIDE, an integrated

fuzzy inference development

environment.

FUZZY CONTROLLER FOR

AIR CONDITIONING SYSTEM:--In

the following discussion, we give

examples of air conditioning systems,

ranging from a basic model to an

advanced model. We do not provide FIU

(Fuzzy Inference Unit) source code as

we have in previous application notes.

Instead, this time we concentrate on the

input/output variables of the fuzzy

controller for an air conditioning system.

A Basic Model:--Let us start with the

simplest air conditioning system, which

is shown in Figure 1. The only control

target in this system is temperature.

There are two adjustment valves to

change temperature. An example

provided in directory

/fide/examples/fans in the FIDE software

package is similar to this basic model.

 Figure 1 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 

Figure 2 diagrams a fuzzy controller for

an air conditioning system basic model.

Rules for this controller may be

formulated using statements similar to: If

temperature is low then open heating

valve greatlalues such as low are defined

by fuzzy sets (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:--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 in Figure 4

The two 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: 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

the winter, when we use heat to raise

temperature, 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

usedthefollowing rule: If humidity is low

then open humidifying valve slightly.But

its action, if acting as the only rule

forlow humidity, will be late when low

humidity is already the case.

An advanced model for

automobile passenger

environment: Temperature control in

an automobile passenger environment is

more complex than that of a static room

in a building. To address driver and

passenger comfort and safety, many

factors must be taken into account.

Temperature and humidity should be

controlled to provide an enjoyable ride.

However, it is also critical to keep

windows from being fogged, which is

caused by a temperature differential

between inside and outside air in

combination with the interior humidity.

To obtain satisfactory control results, the

strength of sunshine radiation and the

automobile speed must also be factored

in. Figure 5 shows a fuzzy controller

which employs five sensors to obtain

data for temperature control and

humidity control in an automobile. A

recent industry report on the application

of such a controller on a new model

automobile indicates this controller

outperforms conventional control

systems substantially. It prevents rapid

change of temperature in the car when

doors or windows are opened and then

closed. It even reacts to weather changes

because interior humidity changes

caused by the weather can be detected

by sensors. Figure 5 Fuzzy Controller

for Air Conditioning System: Advanced

Model

 

CONCLUSION:--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 and

environments. 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 this

problem. Fuzzy controllers incorporated

in the latest model automobiles designed

by Japanese auto makers provide proof

that temperature control in diverse

environments can be solved. The key to

a good solution lies in thorough analysis

of factors affecting the control target and

the kinds of sensors and sensing

techniques used to detect these factors.

For an engineer, an ideal machine would

be one in which human requests are

automatically interpreted and responded

to by adjusting itself appropriately to

variations in the environment. Fuzzy

logic can help make this ideal a reality.

At the least, it makes the effort easier.

REFERENCES: --Zadeh L.A., Fuzzy Sets, ‘’Information and Control’’, 8

(1965) 338353.

Ahmad M. Ibrahim ,Introduction to Applied Fuzzy Electronics, , ISBN 0-13-

206400-6--Sahelefarda 22:07, 10 January 2007 (UTC)

www.wikipedia.com,. www.aptronix.com, .www.google.com

Authorised By SANTOSH BHARADWAJ REDDY Email: help@matlabcodes.com Engineeringpapers.blogspot.com

More Papers and Presentations available on above site