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

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ÖZAKIN A., Agambayev A., Farhat M., Herencsar N., Brancik L., Bagci H., ...Daha Fazla

IEEE Access, cilt.7, ss.80233-80246, 2019 (SCI-Expanded) identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 7
  • Basım Tarihi: 2019
  • Doi Numarası: 10.1109/access.2019.2923166
  • Dergi Adı: IEEE Access
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.80233-80246
  • Anahtar Kelimeler: Cauer network, constant phase element, continued fraction expansion, distributed RC network, distributed RL network, Foster network, fractional-order capacitor, fractional-order element, fractional-order inductor, genetic algorithm, impedance optimization, phase optimization, RC network, recursive algorithm, RL network, Valsa network
  • Yıldız Teknik Üniversitesi Adresli: Hayır

Özet

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 ±1°. 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® 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.