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Related Experiment Video

Updated: Aug 27, 2025

Radio Frequency Identification and Motion-sensitive Video Efficiently Automate Recording of Unrewarded Choice Behavior by Bumblebees

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Published on: November 15, 2014

Interval Type-3 Fuzzy Adaptation of the Bee Colony Optimization Algorithm for Optimal Fuzzy Control of an Autonomous

Leticia Amador-Angulo¹, Oscar Castillo¹, Patricia Melin¹

¹Division of Graduate Studies, Tijuana Institute of Technology, TecNM, Tijuana 22414, Mexico.

|September 23, 2022

Summary

This study introduces an Interval Type-3 Fuzzy Logic System (IT3FLS) to optimize the Bee Colony Optimization (BCO) algorithm for autonomous mobile robot trajectory tracking. The hybrid approach enhances fuzzy controller performance, especially under uncertainty.

Keywords:

disturbance intelligent controllers interval type-3 fuzzy logic mobile robot uncertainty

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The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

Published on: October 14, 2017

Area of Science:

Robotics
Artificial Intelligence
Control Systems

Background:

Autonomous Mobile Robots (AMRs) require precise trajectory tracking for effective navigation.
Fuzzy Logic Controllers (FLCs) are widely used but can struggle with parameter optimization and dynamic adaptation.
Existing fuzzy systems (Type-1, Interval Type-2, Generalized Type-2) have limitations in handling complex uncertainties.

Purpose of the Study:

To develop a hybrid Interval Type-3 Fuzzy Logic System (IT3FLS) integrated with the Bee Colony Optimization (BCO) algorithm.
To dynamically adapt the alpha (α) and beta (β) parameters of the BCO algorithm using IT3FLS.
To optimize the membership functions (MFs) of an FLC for improved trajectory tracking in AMRs.

Main Methods:

A novel hybrid approach combining IT3FLS with BCO for FLC parameter tuning.
Comparative analysis of the proposed FBCO-IT3FLS against FBCO-T1FLS, FBCO-IT2FLS, and FBCO-GT2FLS.
Introduction of external disturbances to evaluate controller robustness and performance under uncertainty.
Utilization of performance metrics including RMSE, MSE, ITAE, IAE, ISE, and ITSE.

Main Results:

The FBCO-IT3FLS demonstrated superior performance in adapting the α and β parameters compared to other fuzzy logic system types.
The proposed method achieved excellent results in trajectory tracking for the Autonomous Mobile Robot.
Performance improvements were particularly evident when the system operated under introduced disturbances, indicating enhanced robustness.

Conclusions:

The hybrid IT3FLS-BCO approach offers a significant advancement in optimizing fuzzy controllers for AMR trajectory tracking.
IT3FLS provides superior dynamic adaptation capabilities for BCO parameters, outperforming Type-1, Interval Type-2, and Generalized Type-2 fuzzy systems.
The developed controller exhibits enhanced robustness and accuracy, especially in the presence of system uncertainties and disturbances.