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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish
Title of host publicationGECCO 2021 Companion - Proceedings of the 2021 Genetic and Evolutionary Computation Conference Companion
PublisherAssociation for Computing Machinery, Inc
Pages1937-1943
Number of pages7
ISBN (Electronic)9781450383516
DOIs
Publication statusPublished - 2021 Jul 7
Event2021 Genetic and Evolutionary Computation Conference, GECCO 2021 - Virtual, Online, France
Duration: 2021 Jul 102021 Jul 14

Publication series

NameGECCO 2021 Companion - Proceedings of the 2021 Genetic and Evolutionary Computation Conference Companion

Conference

Conference2021 Genetic and Evolutionary Computation Conference, GECCO 2021
Country/TerritoryFrance
CityVirtual, Online
Period21/7/1021/7/14

Keywords

  • differential evolution
  • inverted pendulum
  • nonlinear control
  • optimization
  • particle swarm
  • PID tunning

ASJC Scopus subject areas

  • Computer Science Applications
  • Software
  • Computational Theory and Mathematics

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