A unified general type‐2 fuzzy PID controller and its comparative with type‐1 and interval type‐2 fuzzy PID controller

Abstract

For the type reduction of general type-2 fuzzy sets based on α-plane representation was converted to type reduction of several interval type-2 fuzzy sets, so it was time consuming in real applications. In this paper, a unified general type-2 fuzzy PID (UGT2-FPID) controller using the upper and lower bounds of one α-plane is proposed, which has higher real time. The UGT2-FPID controller contains another two adjust parameters, and the analytical structure of UGT2-FPID controller is obtained by adapting input combination method. Furthermore, this paper discusses the parameter adjustment methods to achieve better controlling effects by the mathematical expressions of UGT2-FPID controller. The robustness and effectiveness of proposed general type-2 fuzzy PID controller are tested by two production process objects. The simulation results show that the controlling effects of proposed general type-2 fuzzy PID controller are better than PID, type-1 fuzzy PID, and interval type-2 fuzzy PID controller even if there are exit uncertainties, like controlled object structure changing, controller disturbance, or output noise.

Interval Type-3 Fuzzy Inference System Design for Medical Classification Using Genetic Algorithms

“…In Ref. [17], a General Type-2 fuzzy PID (proportional integral derivative) controller is presented and compared versus PID, Type-1 fuzzy PID, and Interval Type-2 fuzzy PID using uncertainties such as controller disturbance or output noise, and the proposed PID achieved better results than the other methods shown. It is also important to mention the combination that has been made of Type-2 fuzzy logic with the Internet of Things (IoT).…”

Generalized Type 2 Fuzzy Differential Evolution Applied to a Sugeno Controller

“…The design of discrete noniterative algorithms for the center-of-sets-type reduction of general type 2 fuzzy logic systems can be found in [18]. A unified general type 2 fuzzy PID (UGT2-FPID) controller using the upper and lower bounds of one α-plane is proposed in [19]. The UGT2-FPID controller contains another two adjustment parameters, and the analytical structure of a UGT2-FPID controller is obtained by adapting an input combination method [19].…”

Experimental validation of fuzzy type‐2 against type‐1 scheme applied in DC/DC converter integrated to a PEM fuel cell system

“…Fuzzy structures were established by the discoverer Lofti Zadeh, which are known as fuzzy sets type-1 and -2 (in control systems, these are known as FLC-T1 and FLC-T2). 35,36 The latter mentioned has the advantage to handle uncertain definitions in which the designer has doubts and this ambiguity can be mirrored numerically during the controller's initial configuration. Fuzzy sets type-3 also has been proposed by several researchers.…”

“…Authors Samadi and Rakhtala 34 have proven that FLC can show great performance especially when it is applied to power converters. Fuzzy structures were established by the discoverer Lofti Zadeh, which are known as fuzzy sets type‐1 and ‐2 (in control systems, these are known as FLC‐T1 and FLC‐T2) 35,36 . The latter mentioned has the advantage to handle uncertain definitions in which the designer has doubts and this ambiguity can be mirrored numerically during the controller's initial configuration.…”