IF A = PS AND B = NS THEN C = ZR IF A = PS AND B = NS THEN D = NS
IF A = PS AND B = NS THEN C = ZR IF A = PS AND B = NS THEN D = NS
can be combined into one rule:
IF A = PS AND B = NS THEN C = ZR and D = NS This rule gives two outcomes, thus invoking two defuzzification processes,
one for each controlling output. It is easiest, however, to create each rule individually (with only one outcome) and then combine them later. If at any point during the rule definition you are uncertain of the operational knowl- edge required for that particular rule, you should consult a knowledgeable operator so that he/she can provide you with more input information.
As mentioned earlier, you may or may not have a choice of output member- ship function shapes ( Λ , Π , S, or Z). You also may or may not have a choice about whether the functions are continuous or discrete (see Figure 17-61).
0 Output Data
0 Output Data
Figure 17-61. Fuzzy output sets with (a) continuous and (b) noncontinuous membership
functions.
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S ECTION Advanced PLC Fuzzy C HAPTER 5 Topics and Networks
Logic 17
Remember that, before defuzzification occurs, the fuzzy controller adds all the outcomes based on the appropriate logic. If the rule contains a logical AND function, the controller will select the lowest output value; if the rule contains an OR function, the controller will select the highest output value.
If an application requires a highly accurate or smooth output, the rules should be designed so that an input condition triggers two or more rules. To do this, either the input membership functions must overlap or two input conditions must influence the same output (see Figure 17-62).
Input X 1
4095 counts Input X
IF X = PL THEN Output Y = ZR 0.3
IF X = ZR THEN Output Y = NL
(a)
Input X 2 IF X 1 = ZR THEN Output Y = PL IF X 2 = ZR THEN Output Y = NS
(b)
Figure 17-62. Two rules triggered by (a) one input in an overlapping membership function and (b) two inputs in two nonoverlapping membership functions.
Defuzzification. During the design of a fuzzy logic system, you may be required to choose a defuzzification method, especially if the output member-
ship function is noncontinuous. Defuzzification methods include the center of gravity (centroid), the left-most maximum, and the right-most maximum (see Figure 17-63). If the selected defuzzification method is the center of
gravity approach, the triggering rules must be arranged so that at least one rule is triggered at all times. Thus, there must always be an output from a rule. The controller will generate an error if there is no output due to a gap in input
condition coverage. Figure 17-64 illustrates two fuzzy input sets with four rules, which have a
potential error condition due to improper coverage of the inputs by the rules defined. For instance, if the X 1 input intersects label ZR at the point where only ZR, and not PL, is triggered (shown as the gap in Figure 17-64a) and
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S ECTION Advanced PLC Fuzzy C HAPTER 5 Topics and Networks
Output 0 Data
Grade 1 NM 0.8
(b)
Selected output is 1170 counts (left-most maximum)
Output 0 Data
Selected output
(c)
is 3510 counts (right-most maximum)
Output 0 Data
Output 0 Data
Selected output is 1852 counts (center of gravity)
Figure 17-63. (a) Seven outputs with the final output selected using (b) the left-most max- imum, (c) the right-most maximum, and (d) the center of gravity methods.
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S ECTION Advanced PLC Fuzzy C HAPTER 5 Topics and Networks
0 Input Data X 1
For this X 1 input, no rule will be triggered
0 Input Data X 2
For this X 2 input, no rule will be triggered Rule
1 IF X 1 = ZR
AND X 2 = NL THEN Output = Y 1
2 IF X 1 = PL
AND X 2 = ZR THEN Output = Y 2
3 IF X 1 = PL
THEN Output = Y 3
4 IF X 2 = NL
THEN Output = Y 4
Figure 17-64. Improper coverage of inputs leading to an error condition.
input X 2 intersects label ZR anywhere in the gap area shown in Figure 17- 64b, no rule will be triggered. Therefore, no output will be generated and an error will occur. Figure 17-65 shows another gap situation where a region with no sensitivity has no label (membership function); thus, no rule can be triggered. To avoid these potential error conditions, the rules should be designed so that there are no gaps in the rules.
0 Input Data
If input occurs, no label is referenced, thus no rule is triggered
Rules
Rules
Covering NL
Covering ZR
Rules Covering PL
Gap with no rule
Figure 17-65.
A gap in a fuzzy input set.
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S ECTION Advanced PLC Fuzzy C HAPTER 5 Topics and Networks
Logic 17
K EY center of gravity method T ERMS centroid
defuzzification fuzzification fuzzy logic fuzzy processing fuzzy set grade label maximum value method membership function rule
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» An Industrial Text Company Publication Atlanta • Georgia • USA
» C HAPTER T HREE L OGI C C ON CEPT S
» 3 -3 P RINCIPLES OF B OOLEAN A LGEBRA AND L OGIC
» 3 -4 PLC C I RCU I T S AN D L OGI C C ON TACT S Y M BOLOGY
» C ONTACT S YMBOLS U SED IN PLC S
» L OADING C O N S I D E R AT I O N S
» M E M O RY C A PA C I T Y AND U T I L I Z AT I O N
» A P P L I C AT I O N M E M O RY
» D AT A T ABLE O R G A N I Z AT I O N
» 6 -2 I /O R ACK E NCLOSURES AND T ABLE M APPING
» I /O R ACK AND T ABLE M APPING E XAMPLE
» 6 -4 P L C I NSTRUCTIONS FOR D ISCRETE I NPUTS
» 6 -6 P L C I NSTRUCTIONS F OR D ISCRETE O UTPUTS
» 7 -3 A NALOG I NPUT D ATA R E P R E S E N TAT I O N
» 7 -4 A NALOG I NPUT D ATA H ANDLING
» 7 -6 O V E RV I E W OF A NALOG O UTPUT S IGNALS
» 7 -8 A NALOG O UTPUT D ATA R E P R E S E N TAT I O N
» 7 -9 A NALOG O UTPUT D ATA H ANDLING
» C HAPTER E IGHT S PECI AL F U N CT I ON I /O AN D S ERI AL C OM M U N I CAT I ON I N T ERFACI N G
» T HERMOCOUPLE I NPUT M ODULES
» E NCODER /C OUNTER I N T E R FA C E S
» S TEPPER M OTOR I N T E R FA C E S
» S ERVO M OTOR I N T E R FA C E S
» N ETWORK I N T E R FA C E M ODULES
» S ERIAL C O M M U N I C AT I O N
» I N T E R FA C E U SES AND A P P L I C AT I O N S
» 9 -3 L ADDER D IAGRAM F O R M AT
» 9 -5 L ADDER R E L AY P ROGRAMMING L ADDER S CAN E V A L U AT I O N
» P ROGRAMMING N O R M A L LY C LOSED I NPUTS
» 9 -1 0 A RITHMETIC I NSTRUCTIONS
» 9 -1 4 N ETWORK C O M M U N I C AT I O N I NSTRUCTIONS
» L ANGUAGES AND I NSTRUCTIONS
» F UNCTION B LOCK D IAGRAM (FBD)
» S EQUENTIAL F UNCTION C H A RT S (SFC)
» P ROGRAMMING L ANGUAGE N O TAT I O N
» P ROGRAMMING N O R M A L LY C LOSED T RANSITIONS
» D IVERGENCES AND C ONVERGENCES
» -1 C ONTROL T ASK D EFINITION
» C REAT I N G F LOWCH ART S AN D O U T PU T S EQU EN CES
» C ONFIGURING THE PLC S YSTEM
» S PECIAL I NPUT D EVICE P ROGRAMMING
» S IMPLE S TA R T /S TOP M OTOR C IRCUIT
» F O RWA R D /R EVERSE M OTOR I NTERLOCKING
» AC M OTOR D RIVE I N T E R FA C E
» L ARGE R E L AY S YSTEM M O D E R N I Z AT I O N
» A NALOG I NPUT C OMPARISON AND D ATA L INEARIZATION
» A NALOG P OSITION R EADING F ROM AN LV D T
» L INEAR I N T E R P O L AT I O N OF N ONLINEAR I NPUTS
» L ARGE B AT C H I N G C ONTROL A P P L I C AT I O N
» -7 S H O RT P ROGRAMMING E XAMPLES
» -1 B ASIC M EASUREMENT C ONCEPTS D ATA I N T E R P R E TAT I O N
» I NTERPRETING C OMBINED E RRORS
» B RIDGE C IRCUIT T ECHNIQUES
» R ESISTANCE T E M P E R AT U R E D ETECTORS ( RT D S )
» -1 P ROCESS C ONTROL B ASICS
» I N T E R P R E TAT I O N OF E RROR
» T RAN SFER F U N CT I ON S AN D T RAN SI EN T R ESPON SES
» D E R I V AT I V E L APLACE T RANSFORMS
» Out () s = ( )( ) In () s Hp () s
» S ECOND -O RDER L AG R ESPONSES
» D IRECT -A CTING C ONTROLLERS
» T WO -P OSITION D ISCRETE C ONTROLLERS
» T HREE -P OSITION D ISCRETE C ONTROLLERS
» -5 P R O P O RT I O N A L C ONTROLLERS (P M ODE )
» PV () s ( 1 + Hc Hp () s () s ) = SP Hc Hp () s () s () s
» CV () t = K I ∫ 0 Edt + CV ( t = 0 )
» CV ( t = 2 ) = K I 0 Edt + ∫ CV ( t = 1 )
» -7 P R O P O RT I O N A L -I NTEGRAL C ONTROLLERS (PI M ODE )
» -8 D E R I VAT I V E C ONTROLLERS (D M ODE ) S TANDARD D E R I V AT I V E C ONTROLLERS
» -9 P R O P O RT I O N A L -D E R I VAT I V E C ONTROLLERS (PD M ODE )
» -1 2 C ONTROLLER L OOP T UNING
» Z IEGLER –N ICHOLS O PEN -L OOP T UNING M ETHOD
» I TA E O PEN -L OOP T UNING M ETHOD
» S O F T WA R E T UNING M ETHODS
» R ULE -B ASED K NOWLEDGE R E P R E S E N T AT I O N
» S T AT I S T I C A L AND P ROBABILITY A N A LY S I S
» -1 I NTRODUCTION TO F UZZY L OGIC
» -2 H I S T O RY OF F UZZY L OGIC
» -3 F UZZY L OGIC O P E R AT I O N
» F U Z Z I F I C AT I O N C OMPONENTS
» F UZZY P ROCESSING C OMPONENTS
» D E F U Z Z I F I C AT I O N C OMPONENTS
» S YSTEM D ESCRIPTION AND O P E R AT I O N
» M EMBERSHIP F UNCTIONS AND R ULE C R E AT I O N
» IF A = PS AND B = NS THEN C = ZR IF A = PS AND B = NS THEN D = NS
» C HAPTER N INETEEN I /O B US N ET WORK S
» -4 D EVICE B US N ETWORKS B YTE -W IDE D EVICE B US N ETWORKS
» B IT -W IDE D EVICE B US N ETWORKS
» F IELDBUS P ROCESS B US N ETWORK
» P ROFIBUS P ROCESS B US N ETWORK
» I /O B US N ETWORK A DDRESSING
» P ANEL E NCLOSURES AND S YSTEM C OMPONENTS
» -3 N OISE , H E AT , AND V O LTA G E R EQUIREMENTS
» T ROUBLESHOOTING PLC I NPUTS
» -2 P L C S IZES AND S COPES OF A P P L I C AT I O N S
» I NPUT /O UTPUT C O N S I D E R AT I O N S
» C ONTROL S YSTEM O R G A N I Z AT I O N
» E Q U I VA L E N T L ADDER /L OGIC D IAGRAMS
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