Abstract
Type-2 fuzzy logic controllers (T2 FLC) can be viewed as an emerging class of intelligent controllers because of their abilities in handling uncertainties; in many cases, they have been shown to outperform their Type-1 counterparts. This paper presents a literature review on recent applications of T2 FLCs. To follow the developments in this field, we first review general T2 FLCs and the most well-known interval T2 FLS algorithms that have been used for control design. Certain applications of these controllers include robotic control, bandwidth control, industrial systems control, electrical control and aircraft control. The most promising applications are found in the robotics and automotive areas, where T2 FLCs have been demonstrated and proven to perform better than traditional controllers. With the development of enhanced algorithms, along with the advancement in both hardware and software, we shall witness increasing applications of these frontier controllers.
Highlights
Fuzzy logic sets were first introduced by Zadeh [1]
We present the new applications of Type-2 fuzzy logic controllers (T2 fuzzy logic controller (FLC)) and introduce the vital and key areas where they have been successfully implemented and used
In reviewing the developed controllers in the literature, we find that in many cases, triangular membership functions are used when there are software or hardware limitations, e.g., when the fuzzy controller needs to be implemented on a circuit, which is more done with triangular functions that do not require a lot of memory resources in hardware
Summary
Fuzzy logic sets were first introduced by Zadeh [1]. Developments in fuzzy logic stimulated the creation of fuzzy logic systems (FLSs), which emerged in many applications in systems modeling and control. Earlier FLS designs were generally categorized as Type-1 fuzzy logic systems (T1 FLSs). To incorporate uncertainty in fuzzy systems, Zadeh introduced Type-2 fuzzy sets (T2 FSs). According to Hisdal in [5], “increased fuzziness in a description means increased ability to handle inexact information in a logically correct manner” This extra uncertainly gives the T2 FLS an extra degree of freedom (DOF). Due to the mathematical complexity of T2 FLSs, a simpler form of them was proposed, called interval Type-2 fuzzy logic systems (IT2 FLS). The major difference is that the defuzzifier block of a T1 FLS is replaced by an output processing block in a T2 FLS This block consists of a type reduction (TR) followed by defuzzification, meaning that the TR is the function that maps a T2 FS to a T1 FS. The remainder of the paper is organized as follows: in Section 2 of this paper, we briefly discuss the most well-known IT2 algorithms; in Section 3, we present a literature review of IT2 FLCs along with their applications; and in Section 4, we draw conclusions and future directions
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