Synthesis and Optimization of Fractional-Order Elements Using a Genetic Algorithm

This study proposes a new approach for the optimization of phase and magnitude responses of fractional-order capacitive and inductive elements based on the mixed integer-order genetic algorithm (GA), over a bandwidth of four-decade, and operating up to 1 GHz with a low phase error of approximately &...

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Main Authors: Aslihan Kartci, Agamyrat Agambayev, Mohamed Farhat, Norbert Herencsar, Lubomir Brancik, Hakan Bagci, Khaled N. Salama
Format: Article
Language:English
Published: IEEE 2019-01-01
Series:IEEE Access
Subjects:
Online Access:https://ieeexplore.ieee.org/document/8736971/
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spelling doaj-ead297e53c1a4f2fafd46ec56d5fae392021-03-30T00:08:04ZengIEEEIEEE Access2169-35362019-01-017802338024610.1109/ACCESS.2019.29231668736971Synthesis and Optimization of Fractional-Order Elements Using a Genetic AlgorithmAslihan Kartci0https://orcid.org/0000-0001-5690-7574Agamyrat Agambayev1https://orcid.org/0000-0002-5078-7417Mohamed Farhat2Norbert Herencsar3https://orcid.org/0000-0002-9504-2275Lubomir Brancik4Hakan Bagci5Khaled N. Salama6Department of Radio Electronics, Brno University of Technology, Brno, Czech RepublicComputer, Electrical and Mathematical Sciences & Engineering Division, King Abdullah University of Science and Technology, Thuwal, Saudi ArabiaComputer, Electrical and Mathematical Sciences & Engineering Division, King Abdullah University of Science and Technology, Thuwal, Saudi ArabiaDepartment of Telecommunications, Brno University of Technology, Brno, Czech RepublicDepartment of Radio Electronics, Brno University of Technology, Brno, Czech RepublicComputer, Electrical and Mathematical Sciences & Engineering Division, King Abdullah University of Science and Technology, Thuwal, Saudi ArabiaComputer, Electrical and Mathematical Sciences & Engineering Division, King Abdullah University of Science and Technology, Thuwal, Saudi ArabiaThis study proposes a new approach for the optimization of phase and magnitude responses of fractional-order capacitive and inductive elements based on the mixed integer-order genetic algorithm (GA), over a bandwidth of four-decade, and operating up to 1 GHz with a low phase error of approximately &#x00B1;1&#x00B0;. It provides a phase optimization in the desired bandwidth with minimal branch number and avoids the use of negative component values, and any complex mathematical analysis. Standardized, IEC 60063 compliant commercially available passive component values are used; hence, no correction on passive elements is required. To the best knowledge of the authors, this approach is proposed for the first time in the literature. As validation, we present numerical simulations using MATLAB<sup>&#x00AE;</sup> and experimental measurement results, in particular, the Foster-II and Valsa structures with five branches for precise and/or high-frequency applications. Indeed, the results demonstrate excellent performance and significant improvements over the Oustaloup approximation, the Valsa recursive algorithm, and the continued fraction expansion and the adaptability of the GA-based design with five different types of distributed RC/RL network.https://ieeexplore.ieee.org/document/8736971/Cauer networkconstant phase elementcontinued fraction expansiondistributed RC networkdistributed RL networkFoster network
collection DOAJ
language English
format Article
sources DOAJ
author Aslihan Kartci
Agamyrat Agambayev
Mohamed Farhat
Norbert Herencsar
Lubomir Brancik
Hakan Bagci
Khaled N. Salama
spellingShingle Aslihan Kartci
Agamyrat Agambayev
Mohamed Farhat
Norbert Herencsar
Lubomir Brancik
Hakan Bagci
Khaled N. Salama
Synthesis and Optimization of Fractional-Order Elements Using a Genetic Algorithm
IEEE Access
Cauer network
constant phase element
continued fraction expansion
distributed RC network
distributed RL network
Foster network
author_facet Aslihan Kartci
Agamyrat Agambayev
Mohamed Farhat
Norbert Herencsar
Lubomir Brancik
Hakan Bagci
Khaled N. Salama
author_sort Aslihan Kartci
title Synthesis and Optimization of Fractional-Order Elements Using a Genetic Algorithm
title_short Synthesis and Optimization of Fractional-Order Elements Using a Genetic Algorithm
title_full Synthesis and Optimization of Fractional-Order Elements Using a Genetic Algorithm
title_fullStr Synthesis and Optimization of Fractional-Order Elements Using a Genetic Algorithm
title_full_unstemmed Synthesis and Optimization of Fractional-Order Elements Using a Genetic Algorithm
title_sort synthesis and optimization of fractional-order elements using a genetic algorithm
publisher IEEE
series IEEE Access
issn 2169-3536
publishDate 2019-01-01
description This study proposes a new approach for the optimization of phase and magnitude responses of fractional-order capacitive and inductive elements based on the mixed integer-order genetic algorithm (GA), over a bandwidth of four-decade, and operating up to 1 GHz with a low phase error of approximately &#x00B1;1&#x00B0;. It provides a phase optimization in the desired bandwidth with minimal branch number and avoids the use of negative component values, and any complex mathematical analysis. Standardized, IEC 60063 compliant commercially available passive component values are used; hence, no correction on passive elements is required. To the best knowledge of the authors, this approach is proposed for the first time in the literature. As validation, we present numerical simulations using MATLAB<sup>&#x00AE;</sup> and experimental measurement results, in particular, the Foster-II and Valsa structures with five branches for precise and/or high-frequency applications. Indeed, the results demonstrate excellent performance and significant improvements over the Oustaloup approximation, the Valsa recursive algorithm, and the continued fraction expansion and the adaptability of the GA-based design with five different types of distributed RC/RL network.
topic Cauer network
constant phase element
continued fraction expansion
distributed RC network
distributed RL network
Foster network
url https://ieeexplore.ieee.org/document/8736971/
work_keys_str_mv AT aslihankartci synthesisandoptimizationoffractionalorderelementsusingageneticalgorithm
AT agamyratagambayev synthesisandoptimizationoffractionalorderelementsusingageneticalgorithm
AT mohamedfarhat synthesisandoptimizationoffractionalorderelementsusingageneticalgorithm
AT norbertherencsar synthesisandoptimizationoffractionalorderelementsusingageneticalgorithm
AT lubomirbrancik synthesisandoptimizationoffractionalorderelementsusingageneticalgorithm
AT hakanbagci synthesisandoptimizationoffractionalorderelementsusingageneticalgorithm
AT khalednsalama synthesisandoptimizationoffractionalorderelementsusingageneticalgorithm
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