An Improved Bee Colony Optimization Algorithm Using a Sugeno–Takagi Interval Type-2 Fuzzy Logic System for the Optimal Design of Stable Autonomous Mobile Robot Controllers

This study proposes an enhanced Sugeno–Takagi interval type-2 fuzzy logic system (SIT2FLS) to find the best values for two important parameters in Bee Colony Optimization (BCO). The aim of this study was to develop a stable controller for a mobile robot utilizing BCO in the fuzzy controller and to d...

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Vydáno v:Symmetry (Basel) Ročník 17; číslo 5; s. 789
Hlavní autoři: Amador-Angulo, Leticia, Melin, Patricia, Castillo, Oscar
Médium: Journal Article
Jazyk:angličtina
Vydáno: Basel MDPI AG 20.05.2025
Témata:
Algorithms
Comparative studies
Controllers
Design optimization
Fuzzy algorithms
Fuzzy control
Fuzzy logic
Fuzzy systems
Magnetic levitation systems
Mathematical functions
Mathematical optimization
Neural networks
Optimization
Optimization algorithms
Parameters
Robots
Search algorithms
Sensors
Swarm intelligence
United States
New Jersey
ISSN:2073-8994, 2073-8994
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Abstract This study proposes an enhanced Sugeno–Takagi interval type-2 fuzzy logic system (SIT2FLS) to find the best values for two important parameters in Bee Colony Optimization (BCO). The aim of this study was to develop a stable controller for a mobile robot utilizing BCO in the fuzzy controller and to determine the best membership functions (MFs) in a type-1 fuzzy logic system (T1FLS) for control. Another objective was to use an SIT2FLS to find the best α and β parameters for BCO to enhance the robot trajectory, which was evaluated through an analysis of the mean squared errors. Three types of perturbations were analyzed and simulated. The performance of the SIT2FLS-FBCO was evaluated and compared to that of the T1FLS-FBCO. In addition, a comparative study was performed to demonstrate that the improved BCO works well when there are disturbances affecting the controller. Finally, it was compared with the Mamdani approach, and an FBCO with an interval type-3 FLS was also developed.
AbstractList This study proposes an enhanced Sugeno–Takagi interval type-2 fuzzy logic system (SIT2FLS) to find the best values for two important parameters in Bee Colony Optimization (BCO). The aim of this study was to develop a stable controller for a mobile robot utilizing BCO in the fuzzy controller and to determine the best membership functions (MFs) in a type-1 fuzzy logic system (T1FLS) for control. Another objective was to use an SIT2FLS to find the best α and β parameters for BCO to enhance the robot trajectory, which was evaluated through an analysis of the mean squared errors. Three types of perturbations were analyzed and simulated. The performance of the SIT2FLS-FBCO was evaluated and compared to that of the T1FLS-FBCO. In addition, a comparative study was performed to demonstrate that the improved BCO works well when there are disturbances affecting the controller. Finally, it was compared with the Mamdani approach, and an FBCO with an interval type-3 FLS was also developed.
Audience Academic
Author Castillo, Oscar
Amador-Angulo, Leticia
Melin, Patricia
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  surname: Castillo
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Snippet This study proposes an enhanced Sugeno–Takagi interval type-2 fuzzy logic system (SIT2FLS) to find the best values for two important parameters in Bee Colony...
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SubjectTerms Algorithms
Comparative studies
Controllers
Design optimization
Fuzzy algorithms
Fuzzy control
Fuzzy logic
Fuzzy systems
Magnetic levitation systems
Mathematical functions
Mathematical optimization
Neural networks
Optimization
Optimization algorithms
Parameters
Robots
Search algorithms
Sensors
Swarm intelligence
Title An Improved Bee Colony Optimization Algorithm Using a Sugeno–Takagi Interval Type-2 Fuzzy Logic System for the Optimal Design of Stable Autonomous Mobile Robot Controllers
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