| Summary: | 碩士 === 大同工學院 === 電機工程研究所 === 81 === The main purpose of this thesis is to utilize the
error-backpropagation algorithm of the conventional
neural network to design a fuzzy logic controller (FLC),
which is a form of rule-based controller. The main
advantage of the proposed FLC design is that the design
procedure is simple and effective, and we can avoid some
complexity and difficulty while designing nonlinear control
systems using conventional methods. Another advantage of
this design method is that we can choose some representative
training patterns to design fuzzy logic controllers and
accquire robustness to some extent by generalization
property. The control rule used in FLC is represented
as ─ Ｒ: IF (premise clause), then (consequence clause).
The premise of a rule is the description of fuzzy subspace
of inputs and its consequence is a input-output
relation. In this thesis, we propose two modified forms
of consequence clause, which are nonlinear input-output
relations and different from the conventional linear form.
One is in the form of two variables' polynomial of first
order and the other is in the form of nonlinear multilayered
neural network. For illustration, we design fuzzy logic
controllers for two nonlinear systems. One is the
inverted pendulum system of second order, and the other is a
typical nonlinear system with unity gain. For the purpose
of practicality, we establish a decision table from the
well-trained fuzzy logic controller about the inverted
pendulum system's model to control the physical inverted
pendulum system.
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