Embed Size (px)
Citation preview
1
Part I: Fuzzy Logic Control
Prof. Marzuki Bin KhalidCAIRO
Fakulti Kejuruteraan ElektrikUniversiti Teknologi Malaysia
Module 3
Case Studies and Applications of Fuzzy Logic
UTM
2
Module 3 Objectives• To understand several fuzzy logic control applications.
• To understand how to apply fuzzy logic in practical applications.
• To be able to understand the implementation of fuzzy logic control in several applications.
• To study several applications of fuzzy logic in consumer products and industrial systems.
• Discussions on the trends of fuzzy logic research and applications.
• At the end of the course the student should understand how fuzzylogic is applied in practical applications.
Fuzzy Logic Control Systems
3
Module 3 Contents
• 3.1 Fuzzy control of an inverted pendulum• 3.2 Fuzzy control of a water bath• 3.3 Fuzzy control of traffic lights• 3.4 Intelligent Diagnosis of Power Transformers• 3.5 Several issues in the application of fuzzy logic for
real-time control• 3.6 Industrial/Commercial examples of fuzzy logic
control– 3.6.1 Fuzzy washing machine– 3.6.2 Canon’s fuzzy auto-focus camera– 3.6.3 Minolta’s fuzzy camera– 3.6.4 Sendai’s fuzzy subway train
• 3.7 Summary of Module 3
4
3.1 Fuzzy Control of an Inverted Pendulum
• An inverted pendulum is a classic example of a nonlinear and an unstable system
• The control objective is to stabilize the inverted pendulum in an upright manner
• Several simulation packages have been developed.• In this course students are required to use a
simulation package developed by Togai InfralogicInc., U.S.A. as an assignment.
• The simulation objectives are:– To understand how to design a fuzzy logic controller– To understand the components of a fuzzy logic
controller– To understand the operations of a fuzzy logic
controller
Case Study #1
5
Initial steps to take when designing a fuzzy controller• Plan your design• What are its objectives?• What are the process inputs and outputs?• What are the inputs to the controller?• What are the controller outputs?
Fuzzy logic inverted pendulum control system
Vertical line∆θ
θ Fuzzy LogicController
+
-
θ
θ
∆θ
vMotorv
Study the system
6
Observing the inverted pendulum system
Bob Mass
Shaft
Motor, v
Vertical Axis
θ
∆θAngular velocity,
Angle
Fuzzy Controller Variables
Input: ~ Angle between pendulum shaftand vertical line, θ
~ Angular velocity of pendulum shaft ,∆θ
Output: ~ Motor current or velocity, v(In this case, it is not necessary to take the change of the control signal as the output)
Through observations the input and output fuzzy variables can be identified. This inverted pendulum has a fixed base.
7
• The fuzzy variables have to be broken into smaller modules which we call quantization.
• Quantize the input and output variables into several modules which we call fuzzy subsets and assign the appropriate labels as given in this example.
• You may quantize your variables according to complexity of the problem (in this case, we quantize each fuzzy variable into five fuzzy subsets).
• Assign appropriate membership functions to each fuzzy subset.
• You may choose any kind of shape or size of the membership functions.
Negative Medium
Negative Small
Zero
Positive Small
Positive Medium
NM
NS
ZE
PS
PM
(a).Gaussian
(b). Triangular
(c). Trapezoidal
Three different shapes of membership functions
Quantize the fuzzy variables
8
• For simplicity let’s choose triangular membership functions for each of the fuzzy subsets of the three fuzzy variables.
Membership Functions
• Membership functions (fuzzy subsets) of the three fuzzy variables. The fuzzy subsets are overlapped by about 25%.
PMPSZENSNM
Pendulum Angle, θ
mθ
00
1
PMPSZENSNM
Motor Velocity, v
mv
00
1
PMPSZENSNM
Angular Velocity, ∆θ
m∆θ
00
1
9
Designing the Inference Engine
• The inference engine consists of the following:– Rule base– Encoding technique (compositional operator)
• We need to develop rules to solve the stabilization problem.• This can be done by observing the states of the pendulum.• Rules are in the form of:
IF Conditions THEN Actions
• The rules can be formulated through experience or through the use of examples given by others (see Module 2).
• In most control problem the encoding technique use is the max-min composition.
10
Example of rules to control the pendulum
∆θ = ΖΕ
v = PM
θ = NM
IF θ =NM AND ∆θ =ZE THEN v=PM
If pendulum angle is negative but medium and the angular velocity is about zero then motor velocity should be position and medium
∆θ = ΖΕ
v = NS
θ = PS
IF θ=PS AND ∆θ =ZE THEN v=NS
If pendulum angle is positive and small and the angular velocity is about zero then motor velocity should be negative small
∆θ = ΖΕ
v = ZE
θ = ZE
IF θ=ZE AND ∆θ =ZE THEN v=ZE
If pendulum angle is about zero and the angular velocity is about zero then motor velocity should be about zero
11
• Basic fuzzy rules for controlling the pendulum.
Antecedents
IF X AND/OR Y (= Antecedents) THEN Z (= Consequent)
PS
PM
NM
NM
NS
NS
ZE
PS
PS
PS NM
NM
NS
NS
ZE
ZE
ZE
PM
PM
PMZE
Pendulum Angle, θ
Consequent
Steady-state rule
Rule Base
12
• Normally we would use matrix for simplicity to observe these rules.
• For a 5x5 antecedents there would be a total of 25 rules.• However, not all the banks in the matrix need to be filled up as
some rules do not necessarily need to be fired.• The basic fuzzy rules to control the pendulum are given as
follows:
Pendulum Angle, θ
PS
PM
NM
NM
NS
NS
ZE
PS
PS
PS NM
NM
NS
ZE
ZE
ZE
PM
PM
PMZE
Consequent
Steady-state rule
NS
13
Choice of Encoding or Inference Techniques
• As discussed we can choose either correlation minimum encoding or correlation product encoding schemes.
• Examples are given here.IF e is PL AND ∆ e is ZE THEN ∆u is PL
e=PL at 0.5 ∆e=ZE at 0.2 ∆u=PL at 0.2
Correlation-minimum-inference procedure
Input Fuzzy Variables Output Fuzzy Variable
me m∆e m∆u
e ∆e ∆u
Correlation-product-inference procedure∆ e
IF e is PL AND ∆e is ZE THEN ∆u is PL
e=PL at 0.5 ∆e=ZE at 0.2Input Fuzzy Variables Output Fuzzy Variable
me m∆e m∆u
e ∆u
m ∆uPL = me
PL • m∆eZE
14
Defuzzification Techniques• There are several defuzzification techniques.• Suppose the centroid defuzzification is used the following will be observed.
Example showing centroid defuzzificationwith correlation-minimum encoding scheme
PS ZE
ZE ZE ZEme
m∆ePS
m∆u
m∆um∆e
me
Value of e at instant t
Value of ∆eat instant t
0 0 0
0 0 0
0
IF e=PS AND ∆e=ZE
IF e=ZE AND ∆e=ZE
THEN ∆u = PS
THEN ∆u = ZE
Fuzzy centroid, ∆u
m∆uZE
m∆uPS
15
Test run the fuzzy controller
• When the control system has been configured, we can test the system.• Run your controller.• If results not satisfactory then make minor modifications or
adjustments to the following:– (1). Scaling factors– (2). Overlap of membership functions– (3). Check if rules are correct– (4). Increase rules if necessary– (5). Increase quantization if necessary
• Re-run the system.
Main objective• To design a Fuzzy Logic Controller to balance the
inverted pendulum at a specific orientation within a limited range.
To control and stabilize the rotary inverted pendulum using fuzzy logic control through:
software simulation (Visual Basic 5.0) and real-time control on hardware via PC-based using DOS platform (Borland C++ 5.02 as editor and iC-96 as compiler)
FUZZY CONTROL OF AN INVERTED ROTARY PENDULUM
SOFTWARE REQUIREMENTS
• Visual Basic 5.0 • Borland C++ 5.02 • iC-96 Compiler V2.3 • MCS-96 Relocator and Linker V2.4• iECM-96 V2.3 • Fuzzy Output weights offline self-tuning
program
HARDWARE REQUIREMENTS
The Micro-controller board UC96-SD version 2.0KRi Inverted pendulum model PP-300
rotary inverted pendulum structureservo drive unitpower supply
FUZZY LOGIC CONTROL SYSTEM DESIGN METHODOLOGY
Start
Study the System-determine objectives-identify process and controller's input and output
Fuzzification-quantize the input and output variables-define the membership function
Inference Mechanism-derive fuzzy control rules- based-define fuzzy inference engine
PerformanceOK ?
End
Yes
No
Defuzzification-choose defuzzification method
Fuzzy ControllerOperation
-Fuzzification-Fuzzy Inference-Defuzzification
Simulation & testing
Parameters Tuning-mapping of membership function-fuzzy inference rules
FUZZY LOGIC CONTROL SYSTEM BLOCK DIAGRAM
FuzzyLogic
Controller1
Motor
Set-point(Vertical line) u θ
derr
errFuzzyLogic
Controller2
∆θθ
v
err2
derr2
∆α
α
αu2
θ
Input: 1) Angle between pendulum shaft and vertical line, 2) Angular Velocity of pendulum shaft, 3) Angle between motor arm and horizontal line, 4) Angular Velocity of motor arm,
Output: 1) Motor PWM, u
∆θ
α∆α
DYNAMIC EQUATIONS OF THE INVERTED PENDULUM
( )
=
0
sinm- 0
+ C 2sin-
2sin +sinm-2sin +
+ m + J cos
cosm sin + Lm +
1111
11o12
1121
112
1121
111 112
1121
1 2
111 1011
111122
12
o1
τθ
θ
θ
θθ
θθθθθ
θ
θθ
θθ
g
m
mLmC
LmLJ
ooo
ooo
λ
&
&
&λ
&λλ&λ
&&
&&
λλ
λλ
~
1
1
1
1u
ad-bc*cg
0ad-bc*dg-
0
+
ad-bccf-ah
ad-bcai
ad-bcce-ag 0
1 0 0 0ad-bcbd-df
ad-bcbi-
ad-bcbg-de 0
0 0 1 0
=
θ
θθ
θ
θ
θ
θ
θ
&
&
&&
&
&&
&
o
o
o
o
[ ]
=
1
10 1 0 0
θ
θθ
θ
&
&o
o
y
REAL TIME FUZZY LOGIC CONTROLLER DESCRIPTION
• Singleton fuzzy output is chosen due to its faster processing speed
∑
∑
=
== n
tn
n
tnn
B
KBZ
1
1*
Bn = the weight of the rule which is fired
Kn = singleton output value for that specific rule
INPUT MEMBERSHIP FUNCTIONS
0 2.7o 5.4o
NM NS ZE PS PM1
0 err
µ
-2.7o-5.4o 0
NM NS ZE PS PM1
0 derr
µ
2.7o 5.4o-2.7o-5.4o
• Input membership functions for both controllers are similar
• Single tone controller does not have output membership function
First Input Membership Function
Second Input Membership Function
FUZZY CONTROL RULESerr \ derr NM NS ZE PS PM
NM 855 837 804 346 0
NS 694 316 281 0 -290
ZE 641 271 0 -288 -600
PS 259 0 -284 -272 -713
PM 0 -324 -763 -796 -852
First FuzzyController
err \ derr NM NS ZE PS PM
NM -698 -539 -425 -250 -155
NS -74 -94 -72 -233 -477
ZE 47 43 12 -41 -52
PS 200 192 254 517 675
PM 226 243 259 396 699
Second Fuzzy Controller
EXPERIMENTAL RESULT OF REAL TIME CONTROL
Pendulum Position Vs Number of Sample
-500-300-100100300500
1 92 183
274
365
456
547
638
729
820
911
1002
Number of Sample
Pend
ulum
Po
sitio
n
Pendulum Velocity Vs Number of Sample
-500-300-100100300500
1
105
209
313
417
521
625
729
833
937
Number of Sample
Pen
dulu
m
Vel
ocity
Arm Position Vs Number of Sample
-500-300-100100300500
1 94 187
280
373
466
559
652
745
838
931
Number of Sample
Arm
Pos
ition
Arm Velocity Vs Number of Sample
-500-300-100100300500
1 103 205 307 409 511 613 715 817 919 1021
Number of Sample
Arm
Vel
ocity
EXPERIMENTAL RESULT AFTER DISTURBANCE IS ADDED
Arm Position Vs Number of Sample
-500-300-100100300500
1 134 267 400 533 666 799 932
Number of Sample
Arm
Pos
ition
Pendululm Position Vs Number of Sample
-300-100100300500
1
101
201
301
401
501
601
701
801
901
1001
Number of Sample
Pen
dulu
m
Posi
tion
Pendulum Velocity Vs Number of Sample
-500-300-100100300500
1
109
217
325
433
541
649
757
865
973
Number of Sample
Pen
dulu
m
Velo
city
Arm Velocity Vs Number of Sample
-500-300-100100300500
1
103
205
307
409
511
613
715
817
919
1021
Number of Sample
Arm
Vel
ocity
EXPERIMENTAL RESULTS WHEN SOME CONTROL RULES ARE TAKEN OUT
Both Controllers with only (3x3) rules, instead of (5x5) rules
Pendulum Position Vs Number of Sample
-500-300-100100300500
111
522
9
343
457
571
685
799
913
Number of Sample
Pen
dulu
m
Pos
ition
Arm Position Vs Number of Sample
-500-300-100100300500
1 131 261 391 521 651 781 911
Number of Sample
Arm
Pos
ition
ANALYSIS OF RESULTS
•• The research has shown The research has shown the robustness of the the robustness of the fuzzy logic controller fuzzy logic controller under disturbances and under disturbances and plant uncertaintiesplant uncertainties
Next project- coming up
• Swing up the inverted pendulum and balance at a specific position
• Using neuro-fuzzy controller for better performance
31
3.2 Application of fuzzy logic control to a water bath temperature control system
• A water bath as shown above is an example of a temperature control system.
• Its objective is to control the temperature of the liquid product in the bath.
• Example of applications are in the production of a variety of drink products such as chocolate drink, strawberry milk products, etc.
• Example of industries are Nestle, Yeoh Hiap Seng, F&N, etc.• A stirrer is used so that the product is evenly mixed and the control of
the temperature is evenly distributed.• This example is intended to show how a fuzzy controller can be
designed for such purpose.• A math-model of the plant can be obtained by deriving from first
principles.
Case Study #2
32
Thermal Transducer A/D Microcomputer
Stirrer
SensorThyristor
Water Bath
Heater AC
Control Signal
Schematic diagram of the water bath system
Control ObjectiveTo control the water/liquid temperature in a water bath following a given set-point.
33
Block diagram of the water-bath temperature control system
Practical system complexity• Non-linearity in sensors and relays• Noise• Disturbance• Non-adiabatic• Controller Limits: 0V(min)-5V(max)• (However, these characterisitcs are not
present in the above math-model exceptfor controller limits)
Sensor
Reference(Desired Temp.)
Fuzzy LogicController
+
-
OutputTemp.
Error signal Control signal (Heater)
Water Bath
Disturbance
)(222.0)(998.0)1( kukyky +=+Math-model of system:
The performance of the system can be measured from the error between the output and the reference
34
Configuration of the fuzzy controller
• Two input variables:– Error in temperature of the liquid, e(k) = y(k) - r(k)– Rate of change of error, ∆e(k) = e(k) - e(k-1)
• One output variable:– Change in the control signal, ∆u(k)
FLC
Error
Derivativeof Error
Change inControl Signal
e
∆e
∆u
35
e, ∆e, and ∆u
LinguisticTerm Label
Positive
Zero
Negative NZP
Quantization of the Fuzzy Variables
For simplicity the 3 fuzzy variables can be broken into 3 fuzzy subsets and the membership functions can be overlapped as follows:
PZN
e
µe
PZN
∆e
µ∆e
PZNµ∆u
∆u
The universe for the 3 variables can be set accordingly through observation.
36
Scaling the universes of discourse
• We need to quantify each universe of discourse correctly within the range of the respective variables.
• Scaling factors can be used to scale the universes of discourse.• They act like gain control.
FLC
Error
Derivativeof Error
Change inControl Signal
e
∆e
∆u
G
G∆e
e
G∆u
37
Determining the rule base
• Every fuzzy logic system must have a rule base.• The rule base is used to infer the actions that need to be taken
based on the current conditions.• Example the centre rule which is the steady-state rule can be
written as follows: IF Error in Temperature is about ZeroAND the Derivative of Error is about ZeroTHEN the Change in Control Input is about Zero(IF e=Z AND ∆e=Z THEN ∆ u=Z)
• A rule can be written in triple form such as this:(Z, Z; Z)
• A matrix of the rule base can be set up as shown.• The 1st row and the 1st column are the antecedents
and the boxes in the matrix are the consequents.
Error, e
Derivativeof Error, ∆e
N Z PNZ
P
ze ∆e ∆u
38
Determining the rules
• The choice of the consequents is based on observation and engineering experience.
• A simple way to fill up the rule base matrix is to use the information given in Module 2.
• Fill up the matrix such that the rule base is complete.
Error, e
Derivativeof Error, ∆e
N Z P
N
Z
P
39
Determining the encoding technique
• In many control application the Mamdani’s max-min composition technique is largely used.
• An error reading that is observed will fire the appropriate rule or rules in the rule base.
• Example 3.1 shows how the minimum encoding technique determines the output suppose the rule (Z,Z;Z) is fired.
• Max composition is used to determine if more than 1 of the same consequent resulted. This occurs when several rules are fired.
• A defuzzification technique will be used to give a crisp output value.
PZN
e
µe
PZN
∆e
µ∆e
PZNµ∆u
e(k)
∆e(k)
∆u
Determined output ∆u(k)
0.6
0.7
0.6
Example 3.1
40
Defuzzification
• Defuzzification is required to produce the actual signal that the plant can use.
• Thus, the fuzzy output value need to be defuzzified.
• The output of the fuzzy controller is usually the change in the control signal.
• The actual control signal to the plant is thus:
u(k+1) = u(k) + ∆u(k)
• In Example 3.1 suppose only one rule is fired (Z, Z; Z) as shown, if the max defuzzificationtechnique is used, the output crisp value is:
µc(z*) ≥ µc(z) for all z ∈Z• For centroid defuzzification, the value is given as
follows:
∑∑
=)(
).(*
z
zzz
C
C
µµ
Max defuzzification
Centroid defuzzification
Z
Z
µc
µc
41
Example 3.2
• Suppose the following rules are fired:Rule1=(P,Z;P), Rule 2= (P,N;Z), Rule 3=(N,P;Z) and Rule 4=(Z,P;P).
• In this case the max-min composition would be computed.
• First the min computation is used to obtain the consequents from each of rules fired which are: Rule 1=P, Rule 2=Z, Rule 3=Z and Rule 4=P.
• This shows that there are 2 consequents for “P” from Rules 1 and 4 and 2 consequents for “Z” from Rules 2 and 3.
• Now the max computation is used for obtaining only 1 “P” consequent and 1 “Z” consequent.
• Then any of the defuzzification technique can be used to get the crisp output value.
• In this example we show how the centroid defuzzification strategy is used to solve this problem.
42
• Consider the first 2 rules fired:
Rule1=(P,Z;P) and Rule 4=(Z,P;P), this means that (e, ∆e; ∆u).• Suppose Rule 1: µP=0.75, µZ =0.3, thus, min (µP, µZ) --> µP at 0.3
PZN
e
µe
PZN
∆e
µ∆e
PZNµ∆u
∆u
PZN
e
µe
PZN
∆e
µ∆e
PZNµ∆u
∆u
P, Z; P Z, P; P
∆u at µP =0.3 ∆u at µP =0.4
0.75
0.3
0.8
0.4
0.40.3
43
• Suppose Rule 4: µZ =0.8, µP=0.4, thus, min (µZ, µP) --> µP at 0.4
• Since the consequent P is fired twice having 2 membership values, 0.3 and 0.4 thus, we need to use the max compositional operator to obtain only 1 P value of the consequent.
• Thus, max (µP=0.3, µP=0.4) will give µP=0.4.
• Similarly for Rules 2 and 3, the same computation will be done to obtain only 1 N consequent (see next slide).
• Consider the next 2 rules fired:Rule2=(P,N;Z) and Rule 3=(N,P;Z)
• Suppose Rule 2: µP=0.5, µN =0.6, thus, min (µP, µN) --> µZ at 0.5• Suppose Rule 3: µN =1.0, µP=0.4, thus, min (µN, µP) --> µZ at 0.4
44
• As the consequent N is fired twice having 2 membership values, 0.5 and 0.4 thus, we need to use the max compositional operator to obtain only 1 N value of the consequent.
• Thus, max (µZ=0.5, µZ=0.4) will give µZ=0.5.• Suppose we use the centroid defuzzification strategy. The next slide
will show how this is done to get the crisp output.
PZN
e
µe
PZN
∆e
µ∆e
PZNµ∆u
∆u
PZN
e
µe
PZN
∆e
µ∆e
PZNµ∆u
∆u
P, N; Z N, P; Z
∆u at µZ =0.5 ∆u at µZ =0.4
0.5
0.6
1.0
0.4
0.40.5
45
• For the example given, suppose the centroid defuzzification technique is used to calculate the crisp output.
• Graphically this can be shown as follows.
• The first consequent P was obtained at µP =0.4 and the second consequent Z at µZ =0.5.
• The centroid defuzzificationalgorithm will calculate the crisp value of ∆u from the following equation:
where k is the sample number across the universe for ∆u.
PZNµ∆u
∆u∆u at µP =0.4
0.4
PZNµ∆u
∆u∆u at µZ =0.5
0.5
PZNµ∆u
∆uCrisp value of ∆u
∑∑
∆
∆=∆
)(
).(
k
kku
u
u
µµ
46
Exercise Based on Simulations
• Study the output of the water bath fuzzy control system at the particular sampling interval.
• Note down the sampling interval, the setpoint (r), the output (y), error (e), and del_error (∆e).
• By studying the rule-base matrix, write down the rules that are fired at this interval in triple form, eg. (P, Z; N), etc..
• Study the membership functions table of e and De and indicate the appropriate alpha cut-set based on the input values of e and ∆e.
47
(continued)
• Supposing the controller is designed using the max-min inference algorithm, show in the graph the firing angles of all the consequents based on the rules fired. Plot the resultant waveform at this sampling interval on a graph paper.
• Using the centroid defuzzification strategy, calculate the output ∆u. (Approximately divide the waveform into discrete samples of 0.5). Show how ∆u is calculated.
48
Membership Function Table of e, ∆e and ∆uof Water Bath Fuzzy Controller
PZN
e
µe
PZN
∆e
µ∆e
PZNµ∆u
∆u
0
0
0
25
25
+2.5
-25
-25
-2.5
1.0
1.0
1.0
0.5
0.5
0.5
+5-5
+50
+50-50
-50
49
3.3 Fuzzy Traffic Lights Control
Case Study #3
• Objective: To control traffic lights that can respond to density of vehicles in an efficient manner.
• At CAIRO a traffic lights control simulator has been developed which can use:
– a fuzzy logic controller– conventional preset timer
50
Control Objectives• To control traffic flow optimally at an isolated
junction• To minimise waiting time• To reduce fuel costs• To reduce waste of man-hours
Simulation Objectives• Understanding fuzzy logic application for traffic
lights control• Understanding the components of a fuzzy logic
controller• Able to set up rules for controlling traffic
conditions• Able to compare and understand the advantages
of fuzzy logic controller over fixed-time controller of traffic lights
Fuzzy Traffic Lights Control
51
Comparison between Conventional and Fuzzy traffic controllers
Conventional traffic lights controller• use a preset cycle time to change lights• combine preset cycle time with proximity sensors
Fuzzy traffic lights controller• mimic human intelligence for control of traffic conditions• example of a traffic lights control rule
IF traffic from the north of the city is HEAVY AND the traffic from the west is LESS THEN allow movement of traffic from the north LONGER.
52
Sensor Readings
Traffic FlowFuzzy LogicController
Control signal
Traffic Lights
Relays/Switches
FLC
Traffic Condition
Lights(Arrival)
Traffic Condition
(Queue)
RAG
Switches
Simulation of Traffic Lights Control
Configuration of the Fuzzy Controller
Fuzzy Traffic Lights Control
53
Counter - Queue -Arrival
FLC
State Machine
Traffic Light Interface
Traffic Lights
Sensors
Block diagram of the fuzzy traffic lights controller
54
Design Assumptions
• Traffic conditions are considered in 4 directions but constraint to two: (1) Arrival and (2) Queue
• Also North and South are assummed as one direction whereas East and West as another
• Only straight traffic flow is considered (no trunings)
• Fuzzy controller has 2 inputs (Quantity of Traffic at Arrival and Queue) and 1 output (Extension of Green Lights of the Arrival Traffic Lights (Green))
• All fuzzy variables are quantized into 4 fuzzy subsets
• The shape of membership functions are trapezoidal at the sides and triangular in the middle.
• Flow density of cars can be controlled for each direction.
CAIRO’s Fuzzy Traffic Lights Simulator
Fuzzy Traffic Lights Control
N
S
W E
55
Fuzzy Input Variables
LinguisticTerm Label
Few Many
Too Many TMMY
FAlmost None AN
Fuzzy Output: Variable:Extension of Green Lights
LinguisticTerm Label
Short MediumLonger L
MS
Zero Z
Fuzzy Controller Design
Quantization of Fuzzy VariablesFor simplicity, we quantize all the three fuzzy variables in a similar way, i.e. into four fuzzy subsets:
Fuzzy Traffic Lights Control
LinguisticTerm Label
Small Many
Large LMS
Very Small VS
Arrival Queue
56
Membership Functions
• For simplicity, triangular membership functions are used for each variable.
• In this case, the cardinals for the universes of discourse are integers.
• The membership functions are overlapped by about 25% to allow smooth transition from 1 fuzzy subset to another.
TMYMYF
No. of Cars
µArrival
AN
LMS
No. of Cars
µQueue
VS
LMS
Extension Time (Secs)
µE.TimeZ
ARRIVAL
QUEUE
EXTENSION TIME (Green)
57
Determining the rules
Arrival
Queue
AN F MY
VS
S
M
L
TMY
• A rule base can be developed in the following manner.• Two examples of rules:
IF there are too many cars (TMY) at the arrival side
AND very small number of cars (VS) queueingTHEN extend the green light longer (L).
IF there are few cars (F) at the arrival side AND very small number of cars (VS) queueingTHEN extend the green light short (S).
• Fill up the rule base matrix as shown:• In this application the centroid defuzzification is used.
Rule#1
Rule#2
58
Simulation procedure for Traffic Lights Simulation
• Get into Windows Environment• Execute the Fuzzy Traffic Control Software (FTC)• Select fuzzy controller and configure your fuzzy controller
appropriately as have been discussed in the class• You need to select BOTH from the controller menu• You need to configure the density of cars for each direction. Try:
N=90, W=50, S=80, E=40• Then select <GO>• Fixed-time controller mode will operate first for 2 minutes• Observe the traffic conditions until the fixed-time controller operation
is completed
59
• Write down the maximum number of cars you observed stopping at the junction (any direction)
• After the fixed-time cycle has finished, the fuzzy controller mode wilautomatically run for 2 minutes
• Observe traffic conditions and write down the maximum number of cars you observed stopping at the junction (any direction)
• Highlight Graph and Plot:– (1). Flow density– (2). Wait Time– (3). Cost Function
• Your fuzzy controller should show better results• If not, you need to reconfigure your fuzzy controller• Try simulating other conditions
3.4 Intelligent Fault Diagnosis of Power Transformers
Type of Research: Contract ResearchGrant: ~ RM90,000Collaborators: Tenaga Nasional Berhad ResearchProject Leader: Professor Dr. Marzuki Bin KhalidResearch Team: (1) Syed Fuad Syed Zain
(2) Wan Yat How(3) Mohd. Aizam Talib
Duration: 1.4.1998 – 31.3.2000 (2 years)
Major faults in transformers cause extensive damage, interruption of electricity supply and results in large revenue losses to power utility company.
Condition monitoring of transformers is an effective technique to identify incipient or potential faults inside the transformers.
Summary
62
Newspaper Report6th February 2000
63
8th March 2000
64
New Transformer Blast
New Sunday Times23rd July 2000
TNB Distribution Sdn. Bhd. Customer Services GM (Selangor) initial investigations revealed a technical fault in one of the RM1.5 million transformers….
65
Transformer Blast at Klangdue to improper maintenance
Estimated losses at RM4 million - TNB
Phase 1 Development of a Database
SoftwareADAPT
Phase 2
Automatic Interpretation using Fuzzy Logic
AI Techniques
Project Overview
Benefits - Immediate impacts• Use of Advanced Technology and local expertise
within Malaysia for solving complex industrial problems
• Increased efficiency and reduced operational costs for power transformers maintenance
• Early detection of abnormalities helps prevent unscheduled outages, equipment damage, and safety hazards.
Benefits - Future impacts
• Savings of outflow of Ringgit with less dependency on foreign consultants
• Increased expertise of local consultants
Dissolved Gas Analysis
• Major power transformers are filled with a fluid that serves as a dielectric media, an insulator, and as a heat transfer agent.
• Normal– slow degradation of the mineral oil to yield certain gases.
• Electrical fault – gases are generated at a much more rapid rate.
• Different patterns of gases are generated due to different intensities of energy dissipated by various faults.
• The gases present in an oil sample make it possible to determinethe nature of fault by the gas types and their amount.
DGA• Key Gas Method• Roger Ratio Method
Purpose :To identify fault type base on the
dissolve gases in oil
Gases are produced by degradation of the oil as a result of elevated temperatures which can be cause by :
lighting severe overloadingswitching transientschemical decomposition of oil or insulationoverheated areas of the windingsbad connections which have a high contact resistance
Oil sample from Transformer
Different patterns of gases are generated due to different intensities of energy dissipated by various faults
Gases generated from oil are :Hydrogen (H2) Methane (CH4)Ethane (C2H6) Ethylene (C2H4)Acetylene ( C2H2) C.Monoxide ( CO)Carbon Dioxide (CO2)
Test Result
DGA
Key Gas Method
-Thermal Fault-Corona-Arcing-Cellulose Insulation Breakdown
Roger Ratio Method
-Thermal decomposition-Partial Discharge-Arcing
•H2 – Corona•O2 and N2 – Non-fault related gases•CO & CO2 – Cellulose insulation breakdown•CH4 & C2H6 – Low temperature oil breakdown•C2H4 – High temperature oil breakdown•C2H2 – Arcing
The ranges of ratio are assigned to different codes which determine the fault type.
Methane / Hydrogen Acetylene / Ethane Ethylene / Ethane
Diagnostic Method
40
60
80
100
120
140
160
180
200
0 1/0 1/1 99 7 06/06 /19 97 01/09 /19 97 25 /12 /19 97 0 1/0 1/1 99 8 0 1/0 6/199 8 11/09 /19 98 11 /11 /19 98
4 5
69 65
958 2
123
199
150
Test Value
H ydrogen S ta tis ticsH ydrogen S ta tis tics
Sam plin g D ate
0
50100150
200250
300350
400450
01/01/1997 06 /06 /1997 01 /09 /1997 25 /12 /1997 01/01/1998 01/06/1998 11 /09 /1998 11 /11 /1998
22 3 7 2 1 3354
3 4 4 1
43 6
Test Value
M e tha n e S tatis tic sM eth a n e S tatistic s
S am p ling D ate
0
10 0
20 0
30 0
40 0
50 0
60 0
70 0
0 1/01 /1 9 97 06 /0 6/19 97 0 1 /0 9/19 97 2 5/12 /1 99 7 0 1 /0 1/19 98 0 1/06 /1 99 8 1 1 /0 9/19 98 1 1/11 /1 99 8
500
323 322
12
651
145
1 4
465
Test Value
E th y le ne S ta tis ticsE th yle ne S tatis tics
S a m p lin g D ate
FUZZY INTERPRETATION
Transformer Condition
☺ Good
Normal
Bad
Automatic Interpretation using Fuzzy Logic
Report & Graphs for Analysis
Crisp to FuzzyFuzzify
Fuzzy Inference Fuzzy to CrispDefuzzify
IF CO=Hi And CO2=Hi
then Condition A
Aggregation
Composition
Real variable to linguistic variable
linguistic variable to real variable
Software output
Reports &
Graphs
Intelligent Fault Diagnosis of Power Transformers by Fuzzy Logic
Type of Research: Contract ResearchGrant: ~ RM90,000Collaborators: Tenaga Nasional Berhad ResearchProject Leader: Professor Dr. Marzuki Bin KhalidResearch Team: (1) Syed Fuad Syed Zain
(2) Wan Yat How(3) Mohd. Aizam Talib
Duration: 1.4.1998 – 31.3.2000 (2 years)
Major faults in transformers cause extensive damage, interruption of electricity supply and results in large revenue losses to power utility company.
Condition monitoring of transformers is an effective technique to identify incipient or potential faults inside the transformers.
Summary
79
Newspaper Report6th February 2000
80
8th March 2000
81
New Transformer Blast
New Sunday Times23rd July 2000
TNB Distribution Sdn. Bhd. Customer Services GM (Selangor) initial investigations revealed a technical fault in one of the RM1.5 million transformers….
82
Transformer Blast at Klangdue to improper maintenance
Estimated losses at RM4 million - TNB
Phase 1 Development of a Database
SoftwareADAPT
Phase 2
Automatic Interpretation using Fuzzy Logic
AI Techniques
Project Overview
Benefits - Immediate impacts• Use of Advanced Technology and local expertise
within Malaysia for solving complex industrial problems
• Increased efficiency and reduced operational costs for power transformers maintenance
• Early detection of abnormalities helps prevent unscheduled outages, equipment damage, and safety hazards.
Benefits - Future impacts
• Savings of outflow of Ringgit with less dependency on foreign consultants
• Increased expertise of local consultants
Dissolved Gas Analysis
• Major power transformers are filled with a fluid that serves as a dielectric media, an insulator, and as a heat transfer agent.
• Normal– slow degradation of the mineral oil to yield certain gases.
• Electrical fault – gases are generated at a much more rapid rate.
• Different patterns of gases are generated due to different intensities of energy dissipated by various faults.
• The gases present in an oil sample make it possible to determinethe nature of fault by the gas types and their amount.
DGA• Key Gas Method• Roger Ratio Method• Nomograph
Purpose :To identify fault type base on the
dissolve gases in oil
Gases are produced by degradation of the oil as a result of elevated temperatures which can be cause by :
lighting severe overloadingswitching transientschemical decomposition of oil or insulationoverheated areas of the windingsbad connections which have a high contact resistance
Oil sample from Transformer
Different patterns of gases are generated due to different intensities of energy dissipated by various faults
Gases generated from oil are :Hydrogen (H2) Methane (CH4)Ethane (C2H6) Ethylene (C2H4)Acetylene ( C2H2) C.Monoxide ( CO)Carbon Dioxide (CO2)
Test Result
DGA
Key Gas Method
-Thermal Fault-Corona-Arcing-Cellulose Insulation Breakdown
Roger Ratio Method
-Thermal decomposition-Partial Discharge-Arcing
•H2 – Corona•O2 and N2 – Non-fault related gases•CO & CO2 – Cellulose insulation breakdown•CH4 & C2H6 – Low temperature oil breakdown•C2H4 – High temperature oil breakdown•C2H2 – Arcing
The ranges of ratio are assigned to different codes which determine the fault type.
Methane / Hydrogen Acetylene / Ethane Ethylene / Ethane
Diagnostic Methods
406080
100120
140160180200
0 1/0 1/1 99 7 06/06 /19 97 01/09 /19 97 25 /12 /19 97 0 1/0 1/1 99 8 0 1/0 6/199 8 11/09 /19 98 11 /11 /19 98
4 5
69 65
958 2
123
199
150
Test Value
H ydrogen S tatis ticsH ydrogen S tatis tics
Sam plin g D ate
0
50100150
200250
300350
400450
0 1/0 1/19 9 7 0 6 /0 6 /1 9 97 0 1 /0 9 /1 9 97 25 /1 2 /1 9 97 0 1/0 1/19 9 8 0 1/0 6/1 99 8 1 1 /0 9 /1 9 98 11 /1 1 /1 9 98
22 3 7 2 1 3354
3 4 4 1
43 6
Test Value
M e tha n e S tatis tic sM eth a n e S tatistic s
S am p ling D ate
0
10 0
20 0
30 0
40 0
50 0
60 0
70 0
0 1/01 /1 9 97 06 /0 6/19 97 0 1 /0 9/19 97 2 5/12 /1 99 7 0 1 /0 1/19 98 0 1/06 /1 99 8 1 1 /0 9/19 98 1 1/11 /1 99 8
500
323 322
12
651
145
1 4
465
Test Value
E th y le ne S ta tis ticsE th yle ne S tatis tics
S a m p lin g D ate
FUZZY INTERPRETATION
Transformer Condition
☺ Good
Normal
Bad
Automatic Interpretation using Fuzzy Logic
Report & Graphs for Analysis
Crisp to FuzzyFuzzify
Fuzzy Inference Fuzzy to CrispDefuzzify
IF CO=Hi And CO2=Hi
then Condition A
Aggregation
Composition
Real variable to linguistic variable
linguistic variable to real variable
Software outputs
Reports &
Graphs
94
3.5 Several issues in the application of fuzzy logic for real-time control
Knowledge required:• Scope of work/project• Whether viable to use fuzzy logic control• Variables that can be measured• Type of actuators• Sensors to be used• PC operating environment• High/Low level programming languages• Hardware knowledge of microchips• Development systems of microchips• Knowledge regarding the process• Digital control theory• Electronics/ Digital electronics• Fuzzy logic control theory• Others
95
Simulation Exercises
• Using the simulation packages developed by CAIRO, try to design your fuzzy controller and understand fuzzy logic control system.
• Observe different rules and membership values used and their impact on the system.
• There are 5 available packages:– 1. Water bath temperature control– 2. Traffic lights control– 3. Coupled-tank liquid-level control– 4. Vfuzzy Version 1.0– 5. Elevator supervisory system
96
Process
Sensor
Actuator
Interface Card
Personal Computer
(Fuzzy Control Algorithm)
High-levelLanguages
(C++, Pascal,Visual Basic, etc.)
(Adclone, NS, etc.)
Application using a personal computer
97
Process
Sensor
Actuator
Personal Computer
Micro-Controller(DSP, Fuzzy Chip,MC68HC11, etc.)
(Download)
A/D
D/A
Application using a microprocessor/microcontroller
Fuzzy control development system or LLL
3.5.1 Applications of Fuzzy Micro-Controller (AL220)
Objective:To use a fuzzy logic micro-controller AL220 in practical applications.
Adaptive Logic AL220
• Analog Micro-Controller• Programmable Analog IC (PAIC)• On-chip A/D and D/A converters• Four 8 bits Analog Inputs and Outputs• EEPROM / ROM versions• Minimum sampling rate up to 0.1 msec• Program directly from INSiGHT IIe Development System
Servo Motor Control Using AL220 micro-controller
GeneratorMotor Drive
LoadTachometer Motor Amplifier
Controller
Coupling Shaft
Flywheel
FeedbackControl Signaly (s) u (s)
PID Control System
Fuzzy Logic Control System
k kS
k Spi
d+ +0 96
08 1.
( . )S +
PID Controller Servo System
SetpointOutput
_+
kp = 2.5 ki = 1.9 kd = 0.72
Fuzzy Logic Controller
Servo SystemSetpoint
Output
_+ e
de
NL ZRNS PLPS
0 Error
1
0
NL ZRNS PLPS
0 Delta Error
1
0
Fuzzy Inputs Variable
NL NS ZR
Error
Delta Error
PS PL
NL
NS
ZR
PS
PL
-5 -2 -1
0
+1
+2
+3
0
+1
+2
+3
+5
-3
-1
0
+1
+2
-3 -2
-1
0
+1
-1
-2
0
Fuzzy rules for Servo System
Fuzzy Logic Control Step Response
100 200 300 400 500 600 700Number of Sampling (100 msec)
PID Control Step Response
010002000300040005000
Vol
t (m
V)
100 200 300 400 500 600 700Number of Sampling (100 msec)
010002000300040005000
Vol
t (m
V)
100 200 300 400 500 600 700 800
Number of Sampling (100 msec)
0
500
1000
1500
2000
2500
3000
3500
4000
4500
5000V
olt (
mV
)
Fuzzy Logic vs PID Control on the Effect of Load Disturbances
Fuzzy Logic
PID
Load Disturbances
106
3.6 Industrial/Commercial Examples of Fuzzy Logic Systems
• Due to the advancement of the microprocessor, many commercial applications of fuzzy logic have been successful since the 1980s.
• In this part of the module, we discuss 3 examples of industrial/commercial applications of fuzzy logic systems.
�Fuzzy washing machine�Canon’s fuzzy auto-focus camera�Minolta’s fuzzy camera�Blood pressure meter�Sendai’s fuzzy subway train
107
Commercial Example #1
• In 1990 Matsushita (National) produced the first automatic controlled fuzzy logic washing machine -“Aisai Go (Beloved Wife) Day Fuzzy”.
• In 1990, the sales were so successful in Japan that it resulted in an explosion of fuzzy logic home consumer’s products such as rice cookers, camcorders, televisions, refrigerators, etc.
3.6.1 Fuzzy logic washing machine
108
Example of a fuzzy logic washing machine
Samsung SW-5AI(S)
109
Example of the Samsung’s SW-5AI(S) Fuzzy logic washing machine
Fuzzy course will select the best washing modeautomatically with one touch of the button based on load, dirt, type of fabric, etc.
110
Principle of the fuzzy washing machine
• The fuzzy logic washing machine operates based on the principle of laundering :“When dirt has been removed, then washing is stopped”
• Using fuzzy inference, the optimum washing time is determined from a wash sensor.
• The wash sensor measures the dirtiness (turbidity) of the water through an optical sensor which is installed near the drain valve.
111
Light(infrared emitting diode)
Receptor
Drained out water
Infrared sensor
A photo-transistor is used as a sensor to measure dirtiness of the water.
112
• The wash sensor consists of an infrared light-emitting diode (LED) and a photo-transistor.
• The light beam generated by the infra-red LED, passing through the wash water in the pipe, enters the phototransistor.
Wash sensor output change over time.
113
• The photo-transistor produces a voltage in proportion to the intensity of the light.
• If the clothes are dirty, the wash water being drained out will be darker, thus less light will be passed.
• The figures below show the light transmittance over time due to the different types of dirt.
• The change of the output of the wash sensor over time is shown in the previous page.
• When washing started, the dirt in the clothes is gradually washed out and the wash water becomes dirty, causing the transmittance of wash water to decrease as shown in (a).
• The rate of decrease of the transmittance depends on the quality of the dirt:
– Fast for muddy dirt
– Slow for oily dirt
114
• This is because muddy dirt is removed easily by the mechanical force of the water flow produced by the rotation of the pulsator, while oily dirt is not adequately removed until the detergent effect takes place.
• When most of the dirt in the clothes has been removed, the transmittance of the wash water approaches a state of saturation.
• The transmittance at saturation becomes lower when the clothes are dirtier, and the transmittance becomes higher when the clothes are less dirty.
115
• It is difficult to obtain the optimum relation between dirtiness and washing time experimentally.
• This is because there are many kinds of dirtiness of clothes which gives different wash sensor output patterns, thus collection through experiments are rather impossible.
• Also the relation between the dirtiness and wash time is not linear.• Hence, fuzzy inference technique would be suitable for such application.
Fuzzy inference in the washing machine
116
• Using fuzzy inference, linguistic rules can be used to solve the washing problem and the nonlinear property of the relation is approximated by an interpolation function of the fuzzy inference
• In order to provide the inference 2 inputs are suitable:– saturation time, Ts
– light transmittance, Vs
• and 1 output– Washing time, Wt
• Saturation time is chosen because it is related to the quality of the dirt and light transmittance is chosen because it is related to the amount of dirt and example of the membership functions are given as follows:
117
• The washing machine can sense the following types of dirt:– Muddy dirt (faster to be washed away)– Oily dirt (need detergent and more time to be washed away)
• When most of dirt has been washed away the washing machine approaches a state of saturation:
– Lower transmittance (heavy dirt)– Higher transmittance (light dirt)
Vs
Ts
Fuzzy rule base Low Middle High
Short
Long T4
T1 T2 T3
T5 T6
118
• Examples of rules (2 rules are given here). A table of rules can be set as shown.
If transmittance (Vs) is Low and Saturation time (Ts) is LongThen Wash time (Wt) is Longer
If transmittance (Vs) is High and Saturation time (Ts) is ShortThen Wash time (Wt) is Shorter
• For simplicity, the consequents of the fuzzy rules are expressed by real numbers, T1 - T6 which are the washing times.
119
Advantages of the fuzzy logic washing machine:
• It would be very difficult to design a rule base system without using fuzzy logic concepts.
• Thus, with fuzzy logic it reduces amount of required memory.
• Rules are acquired from skilled launderers (experts).
• Nonlinear relationship between degree of dirtiness and wash time can be overcome by the nonlinear fuzzy logic controller.
• Excessive or inadequate washing can be avoided.
• Many fuzzy logic washing machine consists of one-touch button system for the fuzzy wash.
120
• This camera was marketed in 1989. • It uses a 4-bit microcontroller with 500-byte of memory.• Fuzzy logic is used to determine the object of focus by evaluating
the field of view and controlling the autofocus mechanism to focus on the object.
• Earlier cameras used the object centred in the field of view as the desired focus.
• This sometimes led to error as in the case of two objects presented off-center.
• This problem had to be solved by the photographer, who focused on one object, locked the auto-focus, and then re-oriented the camera to get the desired shot.
• This manual process is laborious and awkward to the photographer.
Commercial Example #2
3.6.2 Canon’s fuzzy auto-focus camera
121
Range of the Canon family of cameras
122
• To overcome the problem, fuzzy reasoning was introduced in cameras.• First, distances to 3 points in the field of view are measured. Using these
locations and the relationships between them, fuzzy logic decides the desired focus point.
• Using these locations and the relationships between them, fuzzy logic decides where the desired focus lies and then focuses on that point.
• In the Canon auto-focus camera the fuzzy rules were obtained by an analysis of 288 photographs taken by 8 people.
• Examples of some rules are given in the next slide.• One example of the rules is as follows:
If the object is near the Leftthen the plausibility for object to be at Left is Very High.
• Or simply,If L is Near Then Pl is High
(Plausibility means “the chance it will happen”.)
123
Example of 5 rules for the Canon Autofocus camera
124
Example of how rules are fired• Compare figure (a) and (b) below:• We see that figure (a) has main subject on the left and figure (b) has main
subject at the center.• However in both cases, the relationship is L<C<R and both satisfy Rules (b)
and ( e) as given in the rule base in the previous page. • In this case, the decision depends on the values of L, C, and R and this
comparison is done with the help of membership functions.• The actual rule will be fired depending on the highest degree of the
membership function.• It is very difficult for binary logic rules to model this situation which may
require a large number of rules.• However, a few fuzzy rules can easily
deal with this problem.
125
Conclusion• The performance of this method has been evaluated by using 288 pictures
taken by 8 persons.• The percentage of correctly focused pictures, with and without fuzzy rules are
given in the Table below.
Advantages of the fuzzy auto-focus camera:• Reduce the amount of required memory. • Rules are acquired from analysis of about 300 photographs.• Simplicity of usage as compared to conventional auto-focus technique • Results show an improvement of 23% in auto-focusing
Method Focusing rate
Three measured distances + fuzzy
Distance to the center (conventional)
96.5%
73.6%
126
• Minolta Camera Co. Ltd., Japan uses fuzzy logic to combine the 3mechanisms of focusing, zooming and deciding exposure automatically in its cameras since the early 1990s.
• The figure in the next slide shows the mechanism of a typical Minolta fuzzy camera with the above facilities.
• A number of fuzzy modules make up the auto-focus, auto-exposure and auto-zoom facilities in the camera.
Commercial Example #3
3.6.3 Minolta fuzzy auto-focus, auto-exposure and auto-zoom camera
127
Auto-focus, auto-exposure and auto-zooming mechanism of a Minolta Camera, Japan.
FS= Fuzzy logic module
128
Operation Principle of the Minolta Camera
Auto-Focus Module• The fuzzy auto-focusing mode has 6 distance distributions to do the
fuzzy reasoning to locate the main subject, which are obtained by pre-processing the outputs of 4 auto-focus sensors, lens information and 1 sensor that detects the camera position.
• 7 fuzzy rules, obtained from the analysis of approximately 1000 pictures, determine the location of the main subject to focus on.
• By adding the fuzzy logic module for decision making leads to animprovement of 15% in the focus hit rate.
129
Auto-Exposure Module• To implement auto-exposure, fuzzy reasoning is used to determine
exposure value and the best combination of shutter speed and aperture, depending on the type of scene being photographed.
• The exposure value is determined by 3 fuzzy inference modules, using brightness values obtained from 14 zones in the field of view and the position of the main subject (which is determined by the above auto-focus mechanism).
• The first fuzzy module uses the difference in brightness between the main subject and the background to give an output which is a measure of the amount of back-lighting present.
130
• The second fuzzy module decides whether the exposure is to be focused only on the main subject or the entire scene.
• The third fuzzy module uses the outputs of these two fuzzy modules; weighs 3 measures of exposure i.e. at average, at center and at the main subject, and then it outputs the final exposure value.
• The optimal combination of the shutter speed and aperture is determined by fuzzy inference using the type of scene and lens being used (see example in the next slide).
• The type of scene, for example snap, portrait, close-up, or natural scenery, is determined by the distance to the main subject.
• In a scenery shot the depth of field increases.
• Using fuzzy inferencing techniques, fine control according to scenery, lens charatcteristics, etc. can be achieved.
131
Auto-Zooming Module• To implement auto-zooming, fuzzy reasoning is used to decide
the speed at which to zoom the lens.• When the main subject moves, the size of its image is held at
constant value by zooming appropriately to compensate for the movement.
• Fuzzy reasoning chooses the zooming speed by looking at the ratio of current lens magnification to that 1 unit time ago and its rate of change.
• The rules change the speed of the lens, depending on how the object moves.
132
Basic concepts of the inference rules for auto-exposure of the Minolta camera
KEY
L= Large, S = small, B=Big Consequents
Antecedents
133
Blood Pressure Meter• Designed by National, Matsushita Electric Co., Japan• By using fuzzy logic technology, measurements taken are
more reliable and accurate• Measure both systolic and diastolic blood pressure
134
• What is blood pressure?• Blood pressure is the pressure exerted by blood pumped
from the heart on the walls of the blood vessels.• Systolic pressure is the pressure exerted when the heart
contracts and pumps blood into the arteries, while diastolic pressure is the pressure exerted when the heart expands.
• Blood pressure can vary according to a variety of factors including age, sex, and physical condition.
• It is lower during sleep and higher when working.
Importance of Maintaining Normal Blood Pressure
135
• Abnormal blood pressure can indicate a number of illnesses such as:– Cardiovascular problems– Hypertensions– Diabetes– Liver disorder
• Blood pressure will be higher than usual when:– Excited or tense– Taking a bath– Exercising – Cold– Immediately after eating– Smoking tobacco, drinking coffee
• Blood pressure will be lower than usual when– After drinking alcohol– After taking a bath
136
Fuzzy Logic measurement in the Blood Pressure meter
• Blood flow in the veins give pulse-type waveforms. This allows measurement of blood pressure possible using pulse meters such as these.
• A number of factors affect blood pressure measurements such as:– Condition of the blood vessels– Structure of arm
• Thus making using conventional methods difficult.
• National employs fuzzy logic technology which double checks the strength and shape of the pulse wave and then determine the blood pressure.
137
Fuzzy logic inference engine
Pulse wave shape and its rate of change
Pulse wave strength and its rate of change
Fuzzy Inference
Determination of Actual Blood Pressure
Pressure
Puls
e w
ave
Shap
e
Time
Pre
ssur
e
Pulse wave strength
138
Advantages of Fuzzy logic
• Measurement reliability has been improved for people with the following problems whose blood pressure was difficult to measure:– People with weak pulse– People with irregular pulse– People with unstable pulse
• Error in measurement is detected
139
• First proposed in 1978 by Hitachi Ltd.• Granted permission to operate in 1986 after 300,000 simulations and
3,000 empty runs• Improved stop position by 3X• Reduced power setting by 2X• Total power use reduced by 10%• Finally, Hitachi was granted contracts for Tokyo Subway in 1991
Industrial Example #4
3.6.4 The Sendai Fuzzy Logic Subway System
140
• To measure the train performance, the following Performance Indices can be used:
• Traceability• Comfort• Safety• Running Time Margin• Stopping Accuracy
Membership functions
141
142
Constant Speed Control (CSC)• In this mode, trains are started and run at predetermined target speed.
Train Automatic Stopping Control (TASC)• In this mode, trains are stopped in a predetermined target zone of a station
with high accuracy.
• Each component has its own fuzzy inference and rule base.• Rules are developed based on the following procedures:
Step 1 : Provide a description of the typical operating methods used by the operators
Step 2 : Define the performance indices of the systemStep 3 : Define a model for predictingStep 4 : Convert expert human operating methods into control rules
The train has 2 fuzzy components: (1) speed control and (2) stopping control.
143
Example of the fuzzy rules
• For the CSCIf the control command is “Not Changed” And target speed is followed “Good”Then Command should “Not be Changed”
If control command is changed to “Zero”and coasting and riding comfort is “Good”and the target speed is followed “Very Good”Then Command should be changed to “Zero”
• For the TASCIf control command is changed to “Seventh step of braking”and safety against overrun is “Very Bad”Then Command should be changed to “Emergency Braking”
144
Comparison of the train acceleration between the fuzzy logic computer controller and a human controller.The fuzzy controller shows a more optimal response.
145
Comparison of the Stop Gap Measure of the fuzzy controller and the PID controller
146
Comparison between the fuzzy system and manual operation of the Sendai Subway operating from one station to another. It can be observed that the fuzzy system is more optimal.
147
Fuzzy Train Control by Hitachi Ltd.
148
Summary of the Sendai Fuzzy Train
• Fuzzy inference has been used with success in this project.• A number of distinct advantages are present in the fuzzy
train system over conventional train system such as riding comfort, stopping performance, power reduction, etc.
• Improved overall performance.• Human reasoning can be accommodated rather easily.
149
• A brief review of control system basics have been discussed for a better understanding of fuzzy logic purpose in control applications.
• Specific case studies of fuzzy logic control applications have been discussed in this module.
• Several commercial examples have also been discussed.
3.7 Summary of Module 3
