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Abstract In this paper, the performances of fuzzy pro-
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portional-integral-derivative (PID) and classic PID con-
trollers are compared through simulation studies. For this
purpose, the level control of a two interacting tanks system,
temperature control of unstable continuous stirred tank
reactor (CSTR), and pH control of pH neutralization pro-
cess were selected. In the level control process, results
indicated that both of classic and fuzzy PID controllers
have approximately the same performance. However,
adjusting the classic PID controller is simpler than fuzzy
PID controller. Therefore, in simple processes like level
control in two interacting tanks, classic PID controllers are
preferred. In an unstable CSTR, classic PID controller is
not suitable due to the instability of the system. Fuzzy PID
controller is more useful than classic PID controller in this
type of systems. In pH neutralization process, using classic
PID controller is inappropriate because of nonlinearity of
the system and the fuzzy PID controller is more efficient.
Keywords Classic PID controller Fuzzy PID
controller Level control Temperature control of an
unstable CSTR pH control Adaptive fuzzy control1 Introduction
Nowadays, several parameters in the industrial processes
such as temperature, pressure, level, and pH are controlled.
In this paper, different controllers such as classic PID
controller, fuzzy control could be used. PID controllers
have been developed in last half century and are used more
than the other ones. Different types of fuzzy PID control-
lers could be applied in respect to reducing efficiency of
PID controllers for nonlinear systems, high-order systems,
high delay order systems, and complicated systems.
Zadeh introduced fuzzy set theory in 1995 [1], and the
first fuzzy logic control algorithm was implemented by
Mamdani on a steam engine in 1974. In the following
years, fuzzy logic control has been widely used in many
industrial applications successfully and has gained signifi-
cant achievements [2]. 4479
Nowadays, conventional proportional-integral-deriva-
tive (PID) controllers are commonly used in industry due to
their simplicity, clear functionality, and ease of imple-
mentation. Meanwhile, fuzzy control, an intelligent control
method imitating the logical thinking of human and inde-
pendent of accurate mathematical model of the controlled
object, can overcome some shortcomings of the traditional
PID. However, the fuzzy is a nonlinear control and the
output of the controller has the static error [3].
Fuzzy controller design is composed of three important
stages, namely I. knowledge base design, II. tuning of
controller parameters, and III. membership functions. In
order to make the fuzzy controller achieve the prospective
target, we have to adjust these three stages of the fuzzy
controller [4].
Fuzzy controllers have the advantage that can deal with
nonlinear systems and use the human operator knowledge
[5]. However, the tuning of conventional PID parameter
remains a difficult task due to insufficient knowledge of the
analytical process dynamics; as a result, fuzzy controller is
suitable tool for control. Process loops that can benefit from
a nonlinear control response are excellent candidates for
fuzzy control. Since fuzzy logic provides fast responsetimes with virtually no overshoot, loops with noisy process
signals have better stability and tighter control when fuzzy
logic control is applied [6, 7].
There have been numerous articles investigating dif-
ferent schemes of applying fuzzy logic to the design of PID
controllers [8–12], which are generally termed as fuzzy
PID controllers. Fuzzy PID controllers can be classified
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