Energy-saving variable bias current optimization for magnetic bearing using adaptive differential evolution

Syuan Yi Chen, Min Han Song

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

1 Citation (Scopus)

Abstract

This study proposes an adaptive differential evolution (ADE)-based variable bias current control strategy to improve the energy efficiency of an active magnetic bearing (AMB) system. In the AMB system, the drive current is composed of a control current and a superimposed bias current in which the former is controlled by an external controller used to regulate the rotor position while the latter is set as a pre-designed constant used to improve the linearity and dynamic performance. Generally, the bias current causes power loss even if no force is required. In this regard, the ADE-based variable bias current control strategy is proposed to minimize the energy consumption of the AMB control system without altering the control performance. Experimental results demonstrate the high-accuracy control and significant energy saving performances of the proposed method. The energy improvements compared to baseline were 20.24% and 17.65% for the operation periods of 10 s and 50 s, respectively.

Original languageEnglish
Title of host publicationAdvances in Swarm Intelligence - 8th International Conference, ICSI 2017, Proceedings
EditorsYing Tan, Hideyuki Takagi, Yuhui Shi
PublisherSpringer Verlag
Pages466-474
Number of pages9
ISBN (Print)9783319618234
DOIs
Publication statusPublished - 2017 Jan 1
Event8th International Conference on Swarm Intelligence, ICSI 2017 - Fukuoka, Japan
Duration: 2017 Jul 272017 Aug 1

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10385 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other8th International Conference on Swarm Intelligence, ICSI 2017
CountryJapan
CityFukuoka
Period17/7/2717/8/1

Keywords

  • Differential evolution
  • Energy-saving
  • Magnetic bearing
  • Variable bias current optimization

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Chen, S. Y., & Song, M. H. (2017). Energy-saving variable bias current optimization for magnetic bearing using adaptive differential evolution. In Y. Tan, H. Takagi, & Y. Shi (Eds.), Advances in Swarm Intelligence - 8th International Conference, ICSI 2017, Proceedings (pp. 466-474). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10385 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-61824-1_51