A PID controller is the most widely used controller in industry for control applications due to its simple structure and easy parameter adjusting. When the process becomes too complex to be described, a classical PID control methodology does not provide good performance. Therefore, it is incapable of capturing all design objectives and specifications for a wide range of operating conditions and disturbances [1, 2]. For these reasons, under different operating conditions of the controlled systems, various types of online fuzzy self-tuning for PID controller parameters have been presented in several studies to achieve minimum steady-state error and improve the dynamic behavior [3, 4]. Most of these researches focus on the type-1 fuzzy self-tuning (T1FST) of PID controller [4, 5]. It has been noted that the T1FST PID controllers might not be able to
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