The document discusses fuzzy logic control (FLC) as an alternative to conventional control methods, emphasizing its ability to model human operator experience using fuzzy conditional-action rules. It outlines the structure of fuzzy knowledge-based controllers (FKBCs), which consist of modules such as input fuzzification, a fuzzy rule base, an inference engine, and output defuzzification. Additionally, the benefits of FLCs include cost-effectiveness, robustness across various operating conditions, and ease of customization compared to traditional PID controllers.

1. EE-646
Lecture-10
Fuzzy Knowledge Based Controller
(FKBC)
or
Fuzzy Logic Controller (FLC)

2. Introduction
• In conventional control, the amount of control is
determined in relation to a number of data inputs
using a set of equations to express the entire control
process
• Fuzzy logic control (FLC) has been suggested as a
promising alternative approach for control system
design, especially for those systems that are too
complex to analyze by conventional techniques
• The rationale behind FLC is that an experienced
human operator can competently control a process
without the knowledge of its underlying dynamics
(transfer function).
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3. Contd...
• Expressing human experience in the form of a
mathematical formula is a very difficult task, if not an
impossible one. Fuzzy logic provides a simple tool to
interpret this experience into reality.
• The effective control strategies that the human
operator learns through his experience can often be
expressed as a set of condition-action rules (called
fuzzy rules) which describe conditions about the
process state using linguistic terms (i.e., fuzzy sets
such as low, medium, high, slightly positive) &
recommend control actions using linguistic terms such
as increase slightly or decrease moderately
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4. Contd...
• An example of such a rule is as follows :
IF error is negative small and (change-of-error is
positive big or positive medium) THEN decrease the
steam flow slightly.
• Since the first successful application of fuzzy logic to
control (Mamdani, 1974), FLCs have been successfully
applied to a number of applications like heat
exchangers, activated sludge processes, cement kiln
operation, turning processes, water purification
processes, and power systems operation
• Japanese products have FLCs in washing machines,
vacuum cleaners, ACs, and camcorders
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5. FLC Benefits
1. Developing a fuzzy logic controller is cheaper
than developing a model based or other
controller with comparable performance
2. FLCs are more robust than PID controllers
because they can cover a much wider range of
operating conditions than PID controllers
3. FLCs are customizable, because it is easier to
understand and modify their rules, which not
only mimic a human operator's strategies, but
also are expressed in linguistic terms used in
natural language.
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6. Structure of FKBC
• FLCs may have different structures depending upon
the type of application.
• Despite the variety of possible fuzzy controller
structures, the basic form of all common types of
controllers consists of following modules:
• Input fuzzification (Normalization & conversion of
crisp values to fuzzy values)
• Fuzzy rule base (Knowledge base)
• Inference engine (Implication)
• Output defuzzification (Denormalization & fuzzy-to-
crisp conversion)
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7. Structure... Contd
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Normalization
Fuzzification Inference Engine Defuzzification
Denormalization
Data Base
Rule Base
Crisp Process-State Values Crisp O/P control Values

8. Fuzzification Module
• Normalization: It performs a scale transformation
which maps the physical values of the current
process state variables into a normalized UoD.
Not needed when non-normalized domain in
used
• Fuzzification: It converts a point-wise (crisp),
current value of a process state variable into a
fuzzy set. This is done in order to make it
compatible with the fuzzy set representation of
the process s. v. in the rule antecedent
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9. Fuzzification Module...contd
• The design parameter of the fuzzification
module is the choice of fuzzification strategy
which is done according to type of inference
engine or rule firing in a particular FKBC.
• Fuzzification may be compositional based
inference or individual rule based inference
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10. Knowledge Base
• Data Base: Its basic function is to provide the
necessary information for the proper functioning
of the fuzzification module, the rule base, & the
defuzzification module. This info. includes:
 Fuzzy sets (MFs) representing the meaning of
the linguistic values of the process state and
control o/p variables.
 Physical domains & their normalized
counterparts together with the scaling factors
Choice of above info. constitutes the design
parameters for DB
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11. Knowledge Base...contd
• Rule Base: Its basic function is to represent
the control policy of an experienced process
operator &/or control engineer in a structured
way. The info. is produced in the form of set of
rules as
IF <process state> THEN <control output>
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12. Knowledge Base...contd
The design parameters for RB are:
 Choice of process process state and control
o/p variables.
 Choice of the contents of rule-antecedent
and the rule-consequent
 Choice of term sets for the process state and
control o/p variables.
 Derivation of the set of rules
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13. Inference Engine
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14. Defuzzification Module
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