A differential particle scheme and its application to PID parameter tuning of an inverted pendulum

研究成果: Conference contribution

抄録

Gradient-free stochastic optimization algorithms are well-known for finding suitable parameter configurations over independent runs ubiquitously. Attaining low variability of convergence performance through independent runs is crucial to allow further generalization over distinct problem domains. This paper investigates the performance of a differential particle system in stabilizing a nonlinear inverted pendulum under diverse and challenging initial conditions. Compared to the relevant algorithms in the literature, our experiments show the feasibility of achieving lower convergence variability to stabilize a nonlinear pendulum over independent runs and initial conditions within a reasonable computational load.

本文言語English
ホスト出版物のタイトルGECCO 2021 Companion - Proceedings of the 2021 Genetic and Evolutionary Computation Conference Companion
出版社Association for Computing Machinery, Inc
ページ1937-1943
ページ数7
ISBN(電子版)9781450383516
DOI
出版ステータスPublished - 2021 7 7
イベント2021 Genetic and Evolutionary Computation Conference, GECCO 2021 - Virtual, Online, France
継続期間: 2021 7 102021 7 14

出版物シリーズ

名前GECCO 2021 Companion - Proceedings of the 2021 Genetic and Evolutionary Computation Conference Companion

Conference

Conference2021 Genetic and Evolutionary Computation Conference, GECCO 2021
国/地域France
CityVirtual, Online
Period21/7/1021/7/14

ASJC Scopus subject areas

  • コンピュータ サイエンスの応用
  • ソフトウェア
  • 計算理論と計算数学

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