This document describes a student project to develop an air conditioning system that uses fuzzy logic control. It includes sections on fuzzy logic concepts, developing fuzzy rules and membership functions in MATLAB, the structure of the fuzzy controller, and applications of fuzzy logic control including in air conditioning systems. The project aims to control components like the compressor motor speed, fan speed, fin direction and operation mode to maintain temperature while saving energy. It discusses limitations of fuzzy systems and future work developing the hardware system and exploring additional inputs.

1. Department of Computer Engineering
Sandip Foundation's
Sandip Institute of Technology and Research Centre, Nashik
Savitribai Phule Pune University
BE PROJECT
Year 2019 – 2020
Under the Guidance
Prof.
Vivek Waghmare

2. DEVELOPING AIR
CONDITIONING SYSTEM
USING FUZZY LOGIC
PRESENTED BY:- G23
- Sunil Rajput Exam No: 71720728F
- Ashish kumar Singh Exam No: 71324943K
- Ashish Yadav Exam No: 71741665J
- Mayank Patil Exam No: 71550097L

3. TABLE OF CONTENT
ABSTRACT
INTRODUCTION
OBJECTIVE
BASIC CONCEPTS OF FUZZY LOGIC
RULES
FUZZY CONTROL SYSTEM
AIR CONDITIONER
APPLICATION
LIMITATION
CONCLUSION
REFERENCE'S

4. ABSTRACT
Fuzzy logic control was developed to control the compressor
motor speed , fan speed , fin direction and operation mode to maintain
the room temperature at or closed to the set point temperature and
save energy and keep devices from damage. This paper describes the
development of Fuzzy logic algorithm for Air Condition control system.
This system consists of four sensors for feedback control: first for input
electric volt which used to save devices from damage due to alternated
voltages, second for temperature and third for humidity and fourth for
dew point. Simulation of the Fuzzy logic algorithm for Air Condition
controlling system is carried out based on MATLAB.

5. INTRODUCTION
First proposed in 1965 by Lotfi Zadeh as a
way to process imprecise data.
• Fuzzy Logic (FL) controlling system is based
on a set of rules established by an expert.
• These rules are translated into mathematical steps used to
realize a physical controller.
• FL controllers can be physically realized in different forms.
• We adopt look up tables and function realizations
Lotfi Aliasker Zadeh

6. muruganm1@gmail.com
• Instead of using complex mathematical equations
fuzzy logic uses linguistic description to define
the relationship between the input information
and the output action.
• Just as fuzzy logic can be described simply
as “Computing with words rather than
numbers”, fuzzy control can be described
simply as “Control with sentences rather
than equations”.
What is Fuzzy
Logic?

7. muruganm1@gmail.com
Rules :-
Fuzzy logic usually uses IF-THEN rules, or
constructs that are equivalent.
-IF variable is property THEN action
Example:-
A simple temperature regulator that uses a fan might
look like this:
IF temperature is very cold THEN stop fan
IF temperature is cold THEN turn down fan
IF temperature is normal THEN maintain level
IF temperature is hot THEN speed up fan

8. Fuzzy Control System
A fuzzy control system is based on Fuzzy Logic. The
process of designing fuzzy control system can be
described using following steps
Step 1:Identify the principal input, output and process
tasks
Step 2: Identify linguistic variables used and define
fuzzy sets and membershipsaccordingly
Step 3: Use these fuzzy sets and linguistic variables to
form procedural rules
Step 4: Determine the defuzzificationmethod
Step 5: Test the system and modify ifnecessary

9. AirConditioner

10. Controller Structure
• Fuzzification
– Scales and maps input variables to fuzzy sets
• Inference Mechanism
– Approximate reasoning
– Deduces the control action
• Defuzzification
– Convert fuzzy output values to control signals

11. Operations
A B
A  B A  B A

12. Rule Base
• Air Temperature
• Set cold {50, 0, 0}
• Set cool {65, 55, 45}
• Set just right {70, 65, 60}
• Set warm {85, 75, 65}
• Set hot {, 90, 80}
• Fan Speed
• Set stop {0, 0, 0}
• Set slow {50, 30, 10}
• Set medium {60, 50, 40}
• Set fast {90, 70, 50}
• Set blast {, 100, 80}

13. Rules in Matlab

14. Rules and Membership Function via
Matlab

15. Fuzzy Air Conditioner
10
0
20
30
40
50
60
70
80
90
100
0
if
Coldthen
Stop
IFCool then
Slow
IfJustRight
the
nMediu
m
IfWarmthenFast
IfHotthen
Bla
st
1
4
5
5
0
5
5
6
0
6
5
7
0
7
5
8
0
0
8
5
9
0

16. APPLICATIONS
1
6
WashingMachines
Anti-Lock BrakingSystem
Anti sway cranecontrol
Flight Control in planes
In Air-Conditioning
Cutting force optimization in machining

17. Limitations of Fuzzy Systems
Fuzzy systems lack the capability of machine learning
as-well-as neural network type pattern recognition
Verification and validation of a fuzzy knowledge-based
system require extensive testing withhardware
Determining exact fuzzy rules and membership
functions is a hard task
Stability is an important concern for fuzzycontrol

18. CONCLUSION
 Fuzzy Logic provides a completelydifferent, way to approach a control problem.
 Focus on what the system should dorather than trying to understand how it
works.
 Leads to quicker, cheapersolutions.
 In case of the Air-Conditioning system, fuzzy logic helped solve a complex
problem without getting involved in intricate relationships between physical
variables. Intuitive knowledge about input and output parameters was enough to
design an optimally performing system. With most of the problems encountered
in day to day life falling in this category, like washing machines, vacuum
cleaners, etc, fuzzy logic is sure to make a great impact in human life.

19. • Set up the one input system as a proof of concept. We are
in the process of building the hardware set up.
• Based on the first system, make a selection of the
microcontroller models appropriate for a two and three input
system
FUTURE SCOPE

20. REFERENCES
John Yen, Reza Langari, Fuzzy Logic Intelligence, control and Information, Prentice-Hall Inc, 1999
Ali Dr. I.M., 2012. Developing of a Fuzzy Logic Controller for Air Conditioning System, Anbar
Journal for Engineering Sciences, Vol 5, 180-187.
Aprea C., Mastrullu R. and Rrenno C.,2004. Fuzzy control of compressor speed in refrigerant
plant, Int J Refrigerat., Vol 2, pp.134-143.
Arima M., Hara E. H., and Katzberg J. D., 1995. A fuzzy logic and rough sets controller for HVAC
system, IEEE WESCANEX’95, Vol 95, pp 133-138.
Batayneh W., Araidah O. and Bataineh K., 2010. Fuzzy logic approach to provide safe and
comfortable indoor environment, International Journal of Engineering, Science and Technology,
Vol. 2, pp. 65-72.
Becker M., OestreichD., Hasse Hand Litz L 1994. Fuzzy control for temperature and Humidity in
refrigeration systems, IEEE transact, Vol FM-4-2, pp 1607-1611.
Calvino F., Gennusa M. L., Rizzo G., 2004. The control of indoor thermal comfort conditions:
introducing fuzzy adaptive controller, Ener Build, Vol 36, pp. 97-102