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    Abstract In this paper, the performances of fuzzy pro-
    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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