Optimization with Genetic Algorithms of Interval Type-2 Fuzzy Logic controllers for an autonomous wheeled mobile robot: A comparison under different kinds of perturbations

Martínez, Ricardo; Castillo, Oscar; Aguilar, Luis T.

doi:10.1109/fuzzy.2008.4630477

Cited by 16 publications

(8 citation statements)

References 23 publications

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“…Martinez et al [32] described a tracking controller for the dynamic model of a unicycle mobile robot by integrating a kinematic and a torque controller based on type-2 fuzzy logic theory and genetic algorithms. They have tested type-1 and type-2 FLC under two kinds of perturbations and they observed that the responses of type-2 model are much better for higher perturbation that type-1 fuzzy logic controller of the autonomous robot mobile.…”

Section: Applications In Manufacturing Operations and Industriesmentioning

confidence: 99%

Industrial applications of type-2 fuzzy sets and systems: A concise review

Dereli

Baykasoğlu

Altun

et al. 2011

Computers in Industry

201

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Section: Applications In Manufacturing Operations and Industriesmentioning

confidence: 99%

Industrial applications of type-2 fuzzy sets and systems: A concise review

Dereli

Baykasoğlu

Altun

et al. 2011

Computers in Industry

201

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“…Astudillo et al [18] Logic Theory. Martinez et al [19], [20] made the same work as Astudillo but with adding GA for optimization. Leottau and Melgarejo [21] presented an approach for designing an IT2 FLS for a mobile robot application and described how it could be developed involving the use of T1 and T2 fuzzy sets.…”

Section: Related Workmentioning

confidence: 99%

An Optimized Interval Type-2 Fuzzy Logic Control Scheme based on Optimal Defuzzification

Allawi¹,

Abdalla²

2014

IJCA

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In this paper, a new way of optimizing fuzzy logic is introduced. This way is used to optimize the output of Interval Type-2 Fuzzy Logic controller by replacing the Defuzzification stage by the Optimization algorithm. The algorithm chooses the best crisp output variable from the type-reduced set which is the output of the Type-Reduction stage instead of averaging the set extremes which was performed by Defuzzification stage. Artificial Bee Colony optimization algorithm is used to optimize the Interval Type-2 Fuzzy Logic controller to manage the navigation of multiple mobile robots in indoor environments.

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“…Control and robotics are one of the most widely used application fields of fuzzy logic. The advantages of type-2 fuzzy logic controllers (T2-FLCs) over type-1 fuzzy logic controllers (T1-FLC) have also been demonstrated and documented in (Castro et al, 2007;Figueroa et al, 2005;Hagras 2004Hagras , 2007Martinez et al, 2008;Torres & Saez, 2008). Although this kind of works presents improvements in the T2-FLCs performance, it is necessary to propose methodologies where these advances can be reflected in design processes as (Hagras 2008, Leottau & Melgarejo, 2010bMelin & Castillo, 2003;Wu & Tan, 2004) and hardware implementation approaches over embedded devices as (Hagras, 2008;Leottau & Melgarejo, 2010a;Melgarejo & Peña, 2004.…”

Section: Introductionmentioning

confidence: 99%