Fuzzy Robot Controller Tuning with Biogeography-Based Optimization

George Thomas; Paul Lozovyy; Daniel J. Simon

doi:10.1007/978-3-642-21827-9_33

Fuzzy Robot Controller Tuning with Biogeography-Based Optimization

George Thomas, Paul Lozovyy, Daniel J. Simon

Cleveland State University

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

Abstract

Biogeography-based optimization (BBO) is an evolutionary algorithm (EA) based upon the models of biogeography, which describe the relationship between habitat suitability and the migration of species across habitats. In this work, we apply BBO to the problem of tuning the fuzzy tracking controller of mobile robots. This is an extension of previous work, in which we used BBO to tune a proportional-derivative (PD) controller for these robots. We show that BBO can successfully tune the shape of membership functions for a fuzzy controller with both simulation and real world experimental results.

Original language	American English
Journal	24th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems
DOIs	https://doi.org/10.1007/978-3-642-21827-9_33
State	Published - Jun 1 2011

Keywords

Biogeography-based optimization
Fuzzy logic control

Disciplines

Electrical and Computer Engineering
Robotics

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10.1007/978-3-642-21827-9_33License: CC BY

Fuzzy Robot Controller Tuning with Biogeography-Based OptimizatioFinal published version, 1.02 MBLicense: CC BY

Cite this

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title = "Fuzzy Robot Controller Tuning with Biogeography-Based Optimization",

abstract = " Biogeography-based optimization (BBO) is an evolutionary algorithm (EA) based upon the models of biogeography, which describe the relationship between habitat suitability and the migration of species across habitats. In this work, we apply BBO to the problem of tuning the fuzzy tracking controller of mobile robots. This is an extension of previous work, in which we used BBO to tune a proportional-derivative (PD) controller for these robots. We show that BBO can successfully tune the shape of membership functions for a fuzzy controller with both simulation and real world experimental results.",

keywords = "Biogeography-based optimization, Fuzzy logic control",

author = "George Thomas and Paul Lozovyy and Simon, \{Daniel J.\}",

note = "G. Thomas, P. Lozovyy, and D. Simon. (2011). Fuzzy Robot Controller Tuning with Biogeography-Based Optimization. 24th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, 319-327.",

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AU - Simon, Daniel J.

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Y1 - 2011/6/1

N2 - Biogeography-based optimization (BBO) is an evolutionary algorithm (EA) based upon the models of biogeography, which describe the relationship between habitat suitability and the migration of species across habitats. In this work, we apply BBO to the problem of tuning the fuzzy tracking controller of mobile robots. This is an extension of previous work, in which we used BBO to tune a proportional-derivative (PD) controller for these robots. We show that BBO can successfully tune the shape of membership functions for a fuzzy controller with both simulation and real world experimental results.

AB - Biogeography-based optimization (BBO) is an evolutionary algorithm (EA) based upon the models of biogeography, which describe the relationship between habitat suitability and the migration of species across habitats. In this work, we apply BBO to the problem of tuning the fuzzy tracking controller of mobile robots. This is an extension of previous work, in which we used BBO to tune a proportional-derivative (PD) controller for these robots. We show that BBO can successfully tune the shape of membership functions for a fuzzy controller with both simulation and real world experimental results.

KW - Biogeography-based optimization

KW - Fuzzy logic control

UR - https://engagedscholarship.csuohio.edu/enece_facpub/185

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M3 - Article

JO - 24th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems

JF - 24th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems

ER -

Fuzzy Robot Controller Tuning with Biogeography-Based Optimization

Abstract

Keywords

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