Intuit before tuning: Type-1 and type-2 fuzzy logic controllers

Andriy Sarabakha; Changhong Fu; Erdal Kayacan

doi:10.1016/j.asoc.2019.105495

Intuit before tuning: Type-1 and type-2 fuzzy logic controllers

Andriy Sarabakha, Changhong Fu, Erdal Kayacan^*

^*Corresponding author for this work

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaper › Journal article › Research › peer-review

31 Citations (Scopus)

Abstract

Although a considerable amount of effort has been put in to show that fuzzy logic controllers have exceptional capabilities of dealing with uncertainty, there are still noteworthy concerns, e.g., the design of fuzzy logic controllers is an arduous task due to the lack of closed-form input–output relationships which is a limitation to interpretability of these controllers. The role of design parameters in fuzzy logic controllers, such as position, shape, and height of membership functions, is not straightforward. Motivated by the fact that the availability of an interpretable relationship from input to output will simplify the design procedure of fuzzy logic controllers, the main aims in this work are derive fuzzy mappings for both type-1 and interval type-2 fuzzy logic controllers, analyse them, and eventually benefit from such a nonlinear mapping to design fuzzy logic controllers. Thereafter, simulation and real-time experimental results support the presented theoretical findings.

Original language	English
Article number	105495
Journal	Applied Soft Computing Journal
Volume	81
ISSN	1568-4946
DOIs	https://doi.org/10.1016/j.asoc.2019.105495
Publication status	Published - Aug 2019

Keywords

Aerial robotics
Fuzzy mapping
Interval type-2 fuzzy logic controllers
Type-1 fuzzy logic controllers
Unmanned aerial vehicles

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Cite this

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title = "Intuit before tuning: Type-1 and type-2 fuzzy logic controllers",

abstract = "Although a considerable amount of effort has been put in to show that fuzzy logic controllers have exceptional capabilities of dealing with uncertainty, there are still noteworthy concerns, e.g., the design of fuzzy logic controllers is an arduous task due to the lack of closed-form input–output relationships which is a limitation to interpretability of these controllers. The role of design parameters in fuzzy logic controllers, such as position, shape, and height of membership functions, is not straightforward. Motivated by the fact that the availability of an interpretable relationship from input to output will simplify the design procedure of fuzzy logic controllers, the main aims in this work are derive fuzzy mappings for both type-1 and interval type-2 fuzzy logic controllers, analyse them, and eventually benefit from such a nonlinear mapping to design fuzzy logic controllers. Thereafter, simulation and real-time experimental results support the presented theoretical findings.",

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AU - Kayacan, Erdal

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N2 - Although a considerable amount of effort has been put in to show that fuzzy logic controllers have exceptional capabilities of dealing with uncertainty, there are still noteworthy concerns, e.g., the design of fuzzy logic controllers is an arduous task due to the lack of closed-form input–output relationships which is a limitation to interpretability of these controllers. The role of design parameters in fuzzy logic controllers, such as position, shape, and height of membership functions, is not straightforward. Motivated by the fact that the availability of an interpretable relationship from input to output will simplify the design procedure of fuzzy logic controllers, the main aims in this work are derive fuzzy mappings for both type-1 and interval type-2 fuzzy logic controllers, analyse them, and eventually benefit from such a nonlinear mapping to design fuzzy logic controllers. Thereafter, simulation and real-time experimental results support the presented theoretical findings.

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Intuit before tuning: Type-1 and type-2 fuzzy logic controllers

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Keywords

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