Differential Evolutionary Algorithm with Local Search for the Adaptive Periodic-Disturbance Observer Adjustment

Xiao Feng, Hisayoshi Muramatsu, Seiichiro Katsura

研究成果: Article査読

抄録

Periodic disturbances occur during repetitive operations, and compensation for the periodic disturbances is an important issue to realize precise machine works because the periodic disturbances deteriorate the control precision. In addition, the periodic disturbance becomes a frequency-varying periodic disturbance when the periodicity of the operations changes, which makes the compensation difficult. To eliminate the frequency-varying periodic disturbance, an adaptive periodic-disturbance observer (APDOB) was proposed. However, the APDOB has a problem that the design of the APDOB is complicated with six design parameters. This article proposes a differential evolutionary algorithm with local search that optimizes the six design parameters of the APDOB for the optimal frequency-varying periodic disturbance compensation. The proposed method based on a memetic algorithm framework can explore globally using the differential evolutionary algorithm and explore locally using the local search including the Lévy flight. Moreover, the proposed method can reduce the number of the design parameters.

本文言語English
論文番号9280360
ページ(範囲)12504-12512
ページ数9
ジャーナルIEEE Transactions on Industrial Electronics
68
12
DOI
出版ステータスPublished - 2021 12

ASJC Scopus subject areas

  • 制御およびシステム工学
  • 電子工学および電気工学

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