Fuzzy Sets in Dynamic Adaptation of Parameters of a Bee Colony Optimization for Controlling the Trajectory of an Autonomous Mobile Robot

被引：53

作者：

Amador-Angulo, Leticia ^{[1
]}

Mendoza, Olivia ^{[2
]}

Castro, Juan R. ^{[2
]}

Rodriguez-Diaz, Antonio ^{[2
]}

Melin, Patricia ^{[1
]}

Castillo, Oscar ^{[1
]}

机构：

[1] Tijuana Inst Technol, Div Grad Studies & Res, Tijuana 22414, Mexico

[2] Univ Autonoma Baja California, Tijuana 22390, Mexico

来源：

SENSORS | 2016年 / 16卷 / 09期

关键词：

bee colony optimization; fuzzy controller; fuzzy sets; uncertainty; dynamic adaptation; membership functions; perturbation; autonomous mobile robot; ALPHA-PLANE REPRESENTATION; INTERVAL TYPE-2; SYSTEMS; INFORMATION; ALGORITHM; DESIGN; GENERATION; REDUCTION;

D O I：

10.3390/s16091458

中图分类号：

O65 [分析化学];

学科分类号：

070302 ; 081704 ;

摘要：

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.

引用

页数：27

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