DESIGN AND SIMULATION OF FUZZY LOGIC CONTROLLER USING MATLAB

21.04.2018 muruganm1@gmail.com 1
I hopeyourdayis Beautiful…….

TRAINING ON DESIGN AND SIMULATION OFTRAINING ON DESIGN AND SIMULATION OF
FUZZY LOGIC CONTROLLER USINGFUZZY LOGIC CONTROLLER USING
MATLABMATLAB
Dr.M.MURUGANANDAM,M.E.,Ph.D
Associate Professor,
Department of Electrical and Computer Engg,
Institute of Technology,
Wollega University, Nekemte, Ethiopia

Before we beginBefore we beginss……
some intellectual people have saidsome intellectual people have said ……
Precision is not truth.
—Henri Matisse
So far as the laws of mathematics refer to reality, they
are not certain. And so far as they are certain, they do
not refer to reality.
—Albert Einstein
As complexity rises, precise statements lose meaning and
meaningful statements lose precision.
-Lotfi A. Zadeh

muruganm1@gmail.com
TOPICS TO BE DISCUSSED
What is fuzzy and fuzzy logic?
Basic Applications of Fuzzy Logic?
About simulation
What is MATLAB?
MATLAB SIMULINK
Mathematic operation
Power Electronic Drive circuit
Design of Fuzzy Logic Controller
21.04.2018 4

Meaning OF Fuzzy
 Most of the phenomena we encounter everyday areMost of the phenomena we encounter everyday are
impreciseimprecise - the imprecision may be associated with their- the imprecision may be associated with their
shapes, position, color, texture, semantics that describeshapes, position, color, texture, semantics that describe
what they arewhat they are
 Fuzziness primarily describesFuzziness primarily describes uncertainty(partial truth),uncertainty(partial truth),
vague, unclear and imprecisionvague, unclear and imprecision
 The key idea of fuzziness comes from theThe key idea of fuzziness comes from the multi-valuedmulti-valued
logiclogic
 Imprecision raises in several faces,Imprecision raises in several faces, e.g. as a Languagee.g. as a Language
ambiguityambiguity

What is Fuzzy Logic?
 Fuzzy logic (FL) is a way to make machines more intelligentFuzzy logic (FL) is a way to make machines more intelligent
enabling them to reason in a fuzzy manner like humans, Itenabling them to reason in a fuzzy manner like humans, It
resembles human reasoning.resembles human reasoning.
 The approach of FL imitates the way of decision making inThe approach of FL imitates the way of decision making in
humans that involves allhumans that involves all intermediate possibilitiesintermediate possibilities betweenbetween
digital values YES and NO.digital values YES and NO.
 In conventional, computer can takes precise input and producesIn conventional, computer can takes precise input and produces
a definite output as TRUE or FALSE, which is equivalent toa definite output as TRUE or FALSE, which is equivalent to
human’s YES or NO.human’s YES or NO.
 Fuzzy logic is a form of multi-valued logic; it deals withFuzzy logic is a form of multi-valued logic; it deals with
approximate rather than fixed and exact.approximate rather than fixed and exact.
 In contrast with traditional logic theory, where binary sets haveIn contrast with traditional logic theory, where binary sets have
two-valued logic: true or false, fuzzy logic variables may have atwo-valued logic: true or false, fuzzy logic variables may have a
truth value that ranges between 0 and 1truth value that ranges between 0 and 1

• 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?

Lotfi A. Zadeh
 Fuzzy logic, proposed by Lotfy Aliasker Zadeh in 1965,Fuzzy logic, proposed by Lotfy Aliasker Zadeh in 1965,
emerged as tool to deal with uncertain, imprecise oremerged as tool to deal with uncertain, imprecise or
qualitative decision making problems.qualitative decision making problems.
 Lotfi A. Zadeh was a mathematician, computerLotfi A. Zadeh was a mathematician, computer
scientist, electrical engineer, artificial intelligencescientist, electrical engineer, artificial intelligence
researcher and professor emeritus of computerresearcher and professor emeritus of computer
science at the University of California, Berkeley.science at the University of California, Berkeley.
 Zadeh was best known for proposing fuzzyZadeh was best known for proposing fuzzy
mathematics consisting of these fuzzy-related concepts:mathematics consisting of these fuzzy-related concepts:
fuzzy sets, fuzzy logic, fuzzy algorithms, fuzzyfuzzy sets, fuzzy logic, fuzzy algorithms, fuzzy
semantics, fuzzy languages, fuzzy control, fuzzysemantics, fuzzy languages, fuzzy control, fuzzy
systems, fuzzy probabilities, fuzzy events, and fuzzysystems, fuzzy probabilities, fuzzy events, and fuzzy
information.information.
Lotfi Aliasker Zadeh
Born: February 4, 1921
Died : September 6, 2017
(Aged 96)

Lotfi A. Zadeh Google Scholar

 By fuzzifying crisp datafuzzifying crisp data obtained from measurements, Fuzzy
Logic enhances the robustness of a systemrobustness of a system
 Imprecision raises in several faces - for example, as a
semantic ambiguity the statement “the soup is HOTthe soup is HOT” is
ambiguous, but not fuzzy
e.g. [20º,80º]
The temperature of the soup
Hot
The amount of spices used
Definition of
the domain
of
discourse
Transaction to FuzzinessTransaction to Fuzziness
Fuzzy Example

empty half-full full? almost full?
or half-empty? nearly full? ……
Does it
remain
empty?
21.04.2018 11muruganm1@gmail.com
Fuzzy Example
 Fuzzy theory handles nonrandom uncertaintynonrandom uncertainty

Block Diagram of FLC

STEPS IN FUZZY SYSTEM
 Identify the inputs and their ranges
and name them
 Identify the outputs and their ranges
and name them
 Create the degree of fuzzy
membership function for each input
and output
 Construct the rule base that the
system will operate under
 Decide how the action will be executed
by assigning strengths to the rules
 Combine the rules and defuzzify the
output

Fuzzy Logic Membership Function
 Input to the fuzzyInput to the fuzzy
logic is given aslogic is given as
membershipmembership
function, which isfunction, which is
called linguisticcalled linguistic
variablevariable

Linguistic variables(Triangular Membership Function)Linguistic variables(Triangular Membership Function)
and their rangesand their ranges
Linguistic Value Notation Numerical Ranges (Normalised)
Negative Big
Negative Medium
Negative Small
Zero
Positive Small
Positive Medium
Positive Big
NB
NM
NS
Z
PS
PM
PB
[-2.667 -2 -1.333]
[-2 -1.333 -0.6665]
[-1.333 -0.6665 0]
[-0.6665 0 0.6665]
[0 0.6665 1.333]
[0.6665 1.333 2]
[1.333 2 2.667]

Variable Width Membership Function

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

Mamdani versus Sugeno Models
• Most of our examples were for Mamdani Model.
• Another famous model comes from Sugeno.
• Mamdani-style inference, as we have just seen, requires us to find the centroid of a
two-dimensional shape by integrating across a continuously varying function. In
general, this process is not computationally efficient.
• Michio Sugeno suggested to use a single spike, a singleton, as the membership
function of the rule consequent. A singleton, or more precisely a fuzzy singleton, is
a fuzzy set with a membership function that is unity at a single particular point
on the universe of discourse and zero everywhere else.

Surface Viewer

Rule Editor

Rule Viewer

Building System with the
MATLAB Fuzzy Logic Toolbox

APPLICATION OF FUZZY LOGIC

FUZZY LOGIC IN CONTROL SYSTEMS
 Fuzzy Logic provides a more efficient and resourceful way
to solve Control Systems.
 Some Examples
 Temperature Controller
 Motor Speed control system

Fuzzy Logic Applications
The problem is to Change the speed of a heater fan, based on theThe problem is to Change the speed of a heater fan, based on the
room temperature and humidity.room temperature and humidity.
A temperature control system has four settingsA temperature control system has four settings
 Cold, Cool, Warm, and HotCold, Cool, Warm, and Hot
Humidity can be defined byHumidity can be defined by
 Low, Medium, and HighLow, Medium, and High
Using this we can define the fuzzy set.Using this we can define the fuzzy set.
TEMPERATURE CONTROLLER

Fuzzy Logic in a Washing Machine
Fuzzy logic washing machines are
gaining popularity. These machines
offer the advantages of
performance, simplicity,
productivity, and less cost.
Sensors continually monitor
varying conditions inside the
machine and accordingly adjust
operations for the best wash results.
As there is no standard for fuzzy
logic, different machines perform in
different manners.

Fuzzy Logic in a Washing Machine
Fuzzy logic controls the washing process, water intake, water
temperature, wash time, rinse performance, and spin speed. This
optimizes the life span of the washing machine.
Machines even learn from past experience, memorizing programs
and adjusting them to minimize running costs.
Most fuzzy logic machines feature ‘one touch control’. Equipped with
energy saving features .
The fuzzy logic checks for the extent of dirt and grease, the amount of soap
and water to add, direction of spin, and so on.
The machine rebalances washing load to ensure correct spinning. Else, it
reduces spinning speed if an imbalance is detected. Even distribution of
washing load reduces spinning noise. Neurofuzzy logic incorporates
optical sensors to sense the dirt in water and a fabric sensor to detect the
type of fabric and accordingly adjust wash cycle.

Air Conditioner

 Aerospace
o Altitude control of spacecraft, satellite altitude control, flow and mixture
regulation in aircraft vehicles.
 Automotive
o Trainable fuzzy systems for idle speed control, shift scheduling method for
automatic transmission, intelligent highway systems, traffic control, improving
efficiency of automatic transmissions
 Defense
o Underwater target recognition, automatic target recognition of thermal
infrared images, naval decision support aids, control of a hypervelocity
interceptor, fuzzy set modeling of NATO decision making.
 Electronics
o Control of automatic exposure in video cameras, humidity in a clean room, air
conditioning systems, washing machine timing, microwave ovens, vacuum
cleaners.

 Business
 Decision-making support systems, personnel evaluation in a
large company
 Financial
 Banknote transfer control, fund management, stock market
predictions.
 Industrial
 Cement kiln controls heat exchanger control, activated sludge
wastewater treatment process control, water purification plant
control, quantitative pattern analysis for industrial quality
assurance, control of constraint satisfaction problems in
structural design, control of water purification plants
 Chemical Industry
 Control of pH, drying, chemical distillation processes, polymer
extrusion production, a coke oven gas cooling plant

 Manufacturing
 Optimization of cheese production.
 Marine
 Autopilot for ships, optimal route selection, control of
autonomous underwater vehicles, ship steering.
 Medical
 Medical diagnostic support system, control of arterial
pressure during anesthesia, multivariable control of
anesthesia, modeling of neuropathological findings in
Alzheimer's patients, radiology diagnoses, fuzzy
inference diagnosis of diabetes and prostate cancer.

Mining and Metal Processing
 Sinter plant control, decision making in metal forming.
Robotics
 Fuzzy control for flexible-link manipulators, robot arm
control.
Securities
 Decision systems for securities trading.
Signal Processing and Telecommunications
 Adaptive filter for nonlinear channel equalization control of
broadband noise
Transportation
 Automatic underground train operation, train schedule
control, railway acceleration, braking, and stopping

When to use Fuzzy Logic?
• If the system to be modelled in a
linear system which can be
represented by a mathematical
equation or by a series of rules then
straight forward techniquesstraight forward techniques should
be used.
• Alternatively, if the system is
complexcomplex,, fuzzy logic may be the
technique to follow.21.04.2018 33muruganm1@gmail.com

When to use Fuzzy Logic?
• We define a complex systemcomplex system :
– when it is nonlinear, time-variant, ill-
defined;
– when variables are continuous;
– when a mathematical model is either too
difficult to encode or does not exist or is
too complicated and expensive to be
evaluated;
– when noisy inputs;
– and when an expert is available who can
specify the rules underlying the system
behaviour.21.04.2018 34muruganm1@gmail.com

Geology
Mathematics
We develop a general model for
representing several processes in
Mathematics Education (e.g.
learning, mathematical modelling,
problem-solving, etc) involving
fuzziness and uncertainty

Management

Computer

CIVIL

Mechanical

Nursing

Riddles time…Riddles time…

WELCOME BACKWELCOME BACK

Application of Fuzzy Logic inApplication of Fuzzy Logic in
Electrical DrivesElectrical Drives

Power Electronic Drives
• A machine is driven by a power electronic
converter is called power electronic drives.
Electrical drives are
• AC drives
• DC drives
muruganm1@gmail.com21.04.2018 45

ELECTRICAL DRIVES
BLOCK DIAGRAM OF DRIVE SYSTEM
SOURCE
AC/DC
POWER
MODULATOR
MOTOR
CONTROL UNIT
SENSING
UNIT
INPUT COMMAND

b)CHOPPER (dc-to-dc)a)RECTIFIER (ac-to-dc)
POWER MODULATOR FOR
DC DRIVES

CONVERTERS EQUATION
RECTIFIER
CHOPPER
For Single phase semi converter
For Step down chopper
INVERTER
V0= m X Vs m= Ar / Ac
2
1
0
2
21












+−=
α
απ
π
Sin
VV sRMS
AC VOLTAGE CONTROLLER

SIMULATION
What is simulation?
simulation is the discipline of designing a model of an
actual or theoretical physical system, executing the model on a
digital computer, and analyzing the execution output.
Simulation embodies the principle of ``learning by doing'‘
to learn about the system we must first build a model of some
sort and then operate the model.
In other words the process of limitating a real
phenomenon with a set of mathematical formulas.

SIMULATION
Why simulation?
Simulation is often essential in the following cases:
1)The model is very complex with many variables and interacting
components
2)The underlying variables relationships are nonlinear
3)There is no wastage of money due to damage of circuit
components.
4)No limitation in the parameters range during simulation.

The different simulation software
• MATLAB
• OrCAD PSPICE
• MiPower
• Pscad
• Mathcad
• ETAP
• LABVIEW

MATLAB SIMULATION
VIEW
muruganm1@gmail.com21.04.2018 52

muruganm1@gmail.com
MATH FUNCTION
21.04.2018 53

DC MOTOR
where J- Moment of Inertia
B- Friction coefficient
Kt- Torque constant
Kb- Back emf constant
TL- Load torque applied
io- Armature current
Vo- Armature voltage applied
R- Armature resistance and
L- Armature inductance
Eb= Back EMF
Eb
VOLTAGE EQUATION
EQUIVALENT CIRCUIT

DC MOTOR EQUATIONS
VOLTAGE EQUATION
TORQUE EQUATION

TRANSFER FUNCTION FOR DC MOTOR
Motor Parameters
J=0.011;D=0.004
R=0.6 ;L=0.008
K=0.55

DESIGN OF DC MOTOR USING MATLAB

DESIGN OF Fuzzy Logic CONTROLLER
IN MATLAB GUI

FUZZY LOGIC CONTROLLER FOR DC
DRIVE

MATLAB CIRCUIT FOR FUZZY
CONTROLLED DC DRIVE

FUZZY SPEED CONTROLLER

INPUT MEMBERSHIP FUNCTION
PLOTS FOR ERROR

INPUT MEMBERSHIP FUNCTION
PLOTS FOR CHANGE IN ERROR

OUTPUT MEMBERSHIP
FUNCTION PLOTS

FUZZY RULE TABLE

Advantages of fuzzy control
 It can work with less precise inputs
 Fuzzy logic is conceptually easy to
understand.
 It doesn’t need fast processors
 It needs less data storage in form of
membership functions and rules than
conventional lookup table for non-linear
control
 The mathematical concepts behind
fuzzy reasoning are very simple.

Advantages of fuzzy control
 Fuzzy logic is flexible.
 We can create a fuzzy system to match
any set of input-output data.
 Fuzzy systems don't necessarily
replace conventional control methods.
In
 Fuzzy logic is based on natural
language.
 It is more robust than other non-linear
controllers

muruganm1@gmail.com
Dr.M.MURUGANANDAM
Muruganm1@gmail.com
0949529493
Learn by Doing
Excel Thru Experimentation
Lead by Example
Acquire skills and get employed
Update skills and stay employed
THANK YOU
21.04.2018 68

DESIGN AND SIMULATION OF FUZZY LOGIC CONTROLLER USING MATLAB

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DESIGN AND SIMULATION OF FUZZY LOGIC CONTROLLER USING MATLAB