Real-time metaheuristic algorithm for dynamic fuzzification, de-fuzzification and fuzzy reasoning processes

Hasitha R. Jayetileke; W. R. de Mel; Subhas Chandra Mukhopadhyay

doi:10.3390/app12168242

Real-time metaheuristic algorithm for dynamic fuzzification, de-fuzzification and fuzzy reasoning processes

Hasitha R. Jayetileke, W. R. de Mel, Subhas Chandra Mukhopadhyay^*

^*Corresponding author for this work

School of Engineering

Research output: Contribution to journal › Article › peer-review

6 Citations (Scopus)

109 Downloads (Pure)

Abstract

This paper presents a systematic approach to designing a dynamic metaheuristic fuzzy logic controller (FLC) to control a piece of non-linear plant. The developed controller is a multiple-input–multiple-output (MIMO) system. However, with the proposed control mechanism is possible to adapt it to single-input–single-output (SISO) systems as well. During real-time operation, the dynamic behavior of the proposed fuzzy controller is influenced by a metaheuristic particle swarm optimization (PSO) mechanism. Nevertheless, to analyze the performance of the developed dynamic metaheuristic FLC as a piece of non-linear plant, a 1 kW four-wheel independent-drive electric rover is controlled under different road constraints. The test results show that the proposed dynamic metaheuristic FLC maintains the wheel slip ratio of all four wheels to less than 0.35 and a top recorded translational speed of 90 km/h is maintained for a fixed orientation.

Original language	English
Article number	8242
Pages (from-to)	1-38
Number of pages	38
Journal	Applied Sciences
Volume	12
Issue number	16
DOIs	https://doi.org/10.3390/app12168242
Publication status	Published - 2 Aug 2022

Bibliographical note

Copyright the Author(s) 2022. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.

Keywords

fuzzy logic (FL)
particle swarm optimization (PSO)
brushless sensored direct-current motor control
four-wheel independent-drive

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10.3390/app12168242Licence: CC BY

Publisher version (open access)Final published version, 11.2 MBLicence: CC BY

Cite this

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title = "Real-time metaheuristic algorithm for dynamic fuzzification, de-fuzzification and fuzzy reasoning processes",

abstract = "This paper presents a systematic approach to designing a dynamic metaheuristic fuzzy logic controller (FLC) to control a piece of non-linear plant. The developed controller is a multiple-input–multiple-output (MIMO) system. However, with the proposed control mechanism is possible to adapt it to single-input–single-output (SISO) systems as well. During real-time operation, the dynamic behavior of the proposed fuzzy controller is influenced by a metaheuristic particle swarm optimization (PSO) mechanism. Nevertheless, to analyze the performance of the developed dynamic metaheuristic FLC as a piece of non-linear plant, a 1 kW four-wheel independent-drive electric rover is controlled under different road constraints. The test results show that the proposed dynamic metaheuristic FLC maintains the wheel slip ratio of all four wheels to less than 0.35 and a top recorded translational speed of 90 km/h is maintained for a fixed orientation.",

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AU - Jayetileke, Hasitha R.

AU - de Mel, W. R.

AU - Mukhopadhyay, Subhas Chandra

N1 - Copyright the Author(s) 2022. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.

PY - 2022/8/2

Y1 - 2022/8/2

N2 - This paper presents a systematic approach to designing a dynamic metaheuristic fuzzy logic controller (FLC) to control a piece of non-linear plant. The developed controller is a multiple-input–multiple-output (MIMO) system. However, with the proposed control mechanism is possible to adapt it to single-input–single-output (SISO) systems as well. During real-time operation, the dynamic behavior of the proposed fuzzy controller is influenced by a metaheuristic particle swarm optimization (PSO) mechanism. Nevertheless, to analyze the performance of the developed dynamic metaheuristic FLC as a piece of non-linear plant, a 1 kW four-wheel independent-drive electric rover is controlled under different road constraints. The test results show that the proposed dynamic metaheuristic FLC maintains the wheel slip ratio of all four wheels to less than 0.35 and a top recorded translational speed of 90 km/h is maintained for a fixed orientation.

AB - This paper presents a systematic approach to designing a dynamic metaheuristic fuzzy logic controller (FLC) to control a piece of non-linear plant. The developed controller is a multiple-input–multiple-output (MIMO) system. However, with the proposed control mechanism is possible to adapt it to single-input–single-output (SISO) systems as well. During real-time operation, the dynamic behavior of the proposed fuzzy controller is influenced by a metaheuristic particle swarm optimization (PSO) mechanism. Nevertheless, to analyze the performance of the developed dynamic metaheuristic FLC as a piece of non-linear plant, a 1 kW four-wheel independent-drive electric rover is controlled under different road constraints. The test results show that the proposed dynamic metaheuristic FLC maintains the wheel slip ratio of all four wheels to less than 0.35 and a top recorded translational speed of 90 km/h is maintained for a fixed orientation.

KW - fuzzy logic (FL)

KW - particle swarm optimization (PSO)

KW - brushless sensored direct-current motor control

KW - four-wheel independent-drive

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DO - 10.3390/app12168242

M3 - Article

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SN - 2076-3417

VL - 12

SP - 1

EP - 38

JO - Applied Sciences

JF - Applied Sciences

IS - 16

M1 - 8242

ER -

Real-time metaheuristic algorithm for dynamic fuzzification, de-fuzzification and fuzzy reasoning processes

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