Fuzzy Sets in Dynamic Adaptation of Parameters of a Bee Colony Optimization for Controlling the Trajectory of an Autonomous Mobile Robot
A hybrid approach composed by different types of fuzzy systems, such as the Type-1 Fuzzy Logic System (T1FLS), Interval Type-2 Fuzzy Logic System (IT2FLS) and Generalized Type-2 Fuzzy Logic System (GT2FLS) for the dynamic adaptation of the alpha and beta parameters of a Bee Colony Optimization (BCO)...
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doaj-690cf381f860442b8638151d599aa25f2020-11-24T20:43:54ZengMDPI AGSensors1424-82202016-09-01169145810.3390/s16091458s16091458Fuzzy Sets in Dynamic Adaptation of Parameters of a Bee Colony Optimization for Controlling the Trajectory of an Autonomous Mobile RobotLeticia Amador-Angulo0Olivia Mendoza1Juan R. Castro2Antonio Rodríguez-Díaz3Patricia Melin4Oscar Castillo5Division of Graduate Studies and Research, Tijuana Institute of Technology, Tijuana 22414, MexicoUniversidad Autónoma de Baja California, Tijuana 22390, MexicoUniversidad Autónoma de Baja California, Tijuana 22390, MexicoUniversidad Autónoma de Baja California, Tijuana 22390, MexicoDivision of Graduate Studies and Research, Tijuana Institute of Technology, Tijuana 22414, MexicoDivision of Graduate Studies and Research, Tijuana Institute of Technology, Tijuana 22414, MexicoA hybrid approach composed by different types of fuzzy systems, such as the Type-1 Fuzzy Logic System (T1FLS), Interval Type-2 Fuzzy Logic System (IT2FLS) and Generalized Type-2 Fuzzy Logic System (GT2FLS) for the dynamic adaptation of the alpha and beta parameters of a Bee Colony Optimization (BCO) algorithm is presented. The objective of the work is to focus on the BCO technique to find the optimal distribution of the membership functions in the design of fuzzy controllers. We use BCO specifically for tuning membership functions of the fuzzy controller for trajectory stability in an autonomous mobile robot. We add two types of perturbations in the model for the Generalized Type-2 Fuzzy Logic System to better analyze its behavior under uncertainty and this shows better results when compared to the original BCO. We implemented various performance indices; ITAE, IAE, ISE, ITSE, RMSE and MSE to measure the performance of the controller. The experimental results show better performances using GT2FLS then by IT2FLS and T1FLS in the dynamic adaptation the parameters for the BCO algorithm.http://www.mdpi.com/1424-8220/16/9/1458bee colony optimizationfuzzy controllerfuzzy setsuncertaintydynamic adaptationmembership functionsperturbationautonomous mobile robot |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Leticia Amador-Angulo Olivia Mendoza Juan R. Castro Antonio Rodríguez-Díaz Patricia Melin Oscar Castillo |
spellingShingle |
Leticia Amador-Angulo Olivia Mendoza Juan R. Castro Antonio Rodríguez-Díaz Patricia Melin Oscar Castillo Fuzzy Sets in Dynamic Adaptation of Parameters of a Bee Colony Optimization for Controlling the Trajectory of an Autonomous Mobile Robot Sensors bee colony optimization fuzzy controller fuzzy sets uncertainty dynamic adaptation membership functions perturbation autonomous mobile robot |
author_facet |
Leticia Amador-Angulo Olivia Mendoza Juan R. Castro Antonio Rodríguez-Díaz Patricia Melin Oscar Castillo |
author_sort |
Leticia Amador-Angulo |
title |
Fuzzy Sets in Dynamic Adaptation of Parameters of a Bee Colony Optimization for Controlling the Trajectory of an Autonomous Mobile Robot |
title_short |
Fuzzy Sets in Dynamic Adaptation of Parameters of a Bee Colony Optimization for Controlling the Trajectory of an Autonomous Mobile Robot |
title_full |
Fuzzy Sets in Dynamic Adaptation of Parameters of a Bee Colony Optimization for Controlling the Trajectory of an Autonomous Mobile Robot |
title_fullStr |
Fuzzy Sets in Dynamic Adaptation of Parameters of a Bee Colony Optimization for Controlling the Trajectory of an Autonomous Mobile Robot |
title_full_unstemmed |
Fuzzy Sets in Dynamic Adaptation of Parameters of a Bee Colony Optimization for Controlling the Trajectory of an Autonomous Mobile Robot |
title_sort |
fuzzy sets in dynamic adaptation of parameters of a bee colony optimization for controlling the trajectory of an autonomous mobile robot |
publisher |
MDPI AG |
series |
Sensors |
issn |
1424-8220 |
publishDate |
2016-09-01 |
description |
A hybrid approach composed by different types of fuzzy systems, such as the Type-1 Fuzzy Logic System (T1FLS), Interval Type-2 Fuzzy Logic System (IT2FLS) and Generalized Type-2 Fuzzy Logic System (GT2FLS) for the dynamic adaptation of the alpha and beta parameters of a Bee Colony Optimization (BCO) algorithm is presented. The objective of the work is to focus on the BCO technique to find the optimal distribution of the membership functions in the design of fuzzy controllers. We use BCO specifically for tuning membership functions of the fuzzy controller for trajectory stability in an autonomous mobile robot. We add two types of perturbations in the model for the Generalized Type-2 Fuzzy Logic System to better analyze its behavior under uncertainty and this shows better results when compared to the original BCO. We implemented various performance indices; ITAE, IAE, ISE, ITSE, RMSE and MSE to measure the performance of the controller. The experimental results show better performances using GT2FLS then by IT2FLS and T1FLS in the dynamic adaptation the parameters for the BCO algorithm. |
topic |
bee colony optimization fuzzy controller fuzzy sets uncertainty dynamic adaptation membership functions perturbation autonomous mobile robot |
url |
http://www.mdpi.com/1424-8220/16/9/1458 |
work_keys_str_mv |
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