EE-646

Lecture-10

Fuzzy Knowledge Based Controller

(FKBC)

or

Fuzzy Logic Controller (FLC)

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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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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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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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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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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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

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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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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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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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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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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Inference Engine

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Defuzzification Module

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