28-02-2013, 10:36 AM
Fuzzy Logic in Control Systems: Fuzzy Logic Controller, Part I1
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Abstract
During the past several years, fuzzy control has emerged as
one of the most active and fruitful areas for research in the applications
of fuzzy set theory, especially in the realm of industrial processes, which
do not lend themselves to control by conventional methods because of a
lack of quantitative data regarding the input-output relations. Fuzzy
control is based on fuzzy logic-a logical system that is much closer in
spirit to human thinking and natural language than traditional logical
systems. The fuzzy logic controller (FLC) based on fuzzy logic provides a
means of converting a linguistic control strategy based on expert knowledge
into an automatic control strategy. A survey of the FLC is presented;
a general methodology for constructing an FLC and assessing its
performance is described; and problems that need further research are
pointed out. In particular, the exposition includes a discussion of
fuzzification and defuzzification strategies, the derivation of the database
and fuzzy control rules, the definition of fuzzy implication, and an
analysis of fuzzy reasoning mechanisms.
DECISIONMALKOINGGIC
A S WAS noted in Part I of this paper [150], an FLC may be regarded as a means of emulating a skilled
human operator. More generally, the use of an FLC may
be viewed as still another step in the direction of modeling
human decisionmaking within the conceptual framework
of fuzzy logic and approximate reasoning. In this
context, the forward data-driven inference (generalized
modus ponens) plays an especially important role. In what
follows, we shall investigate fuzzy implication functions,
the sentence connectives and and also, compositional
operators, inference mechanisms, and other concepts that
are closely related to the decisionmaking logic of an FLC.
Fuzzy Implication Functions
In general, a fuzzy control rule is a fuzzy relation which
is expressed as a fuzzy implication. In fuzzy logic, there
are many ways in which a fuzzy implication may be
defined. The definition of a fuzzy implication may be
expressed as a fuzzy implication function. The choice of a
fuzzy implication function reflects not only the intuitive
criteria for implication but also the effect of connective
also.
Inference Mechanisms
The inference mechanisms employed in an FLC are
generally much simpler than those used in a typical expert
system, since in an FLC the consequent of a rule is not
applied to the antecedent of another. In other words, in
FLC we do not employ the chaining inference mechanism,
since the control actions are based on one-level
forward data-driven inference (GMP).
DEFUZZIFICATISOTNR ATEGIES
Basically, defuzzification is a mapping from a space of
fuzzy control actions defined over an output universe of
discourse into a space of nonfuzzy (crisp) control actions.
It is employed because in many practical applications a
crisp control action is required.
A defuzzification strategy is aimed at producing a nonfuzzy
control action that best represents the possibility
distribution of an inferred fuzzy control action. Unfortunately,
there is no systematic procedure for choosing a
defuzzification strategy.
FUTURSET UDIEASN D PROBLEMS
In many of its applications, FLC is either designed by
domain experts or in close collaboration with domain
experts. Knowledge acquisition in FLC applications plays
an important role in determining the level of performance
of a fuzzy control system. However, domain experts and
skilled operators do not structure their decisionmaking in
any formal way. As a result, the process of transferring
expert knowledge into a usable knowledge base of an
FLC is time-consuming and nontrivial. Although fuzzy
logic provides an effective tool for linguistic knowledge
representation and Zadeh’s compositional rule of inference
serves as a useful guideline, we are still in need of
more efficient and more systematic methods for knowledge
acquisition.