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

Xiao Feng, Hisayoshi Muramatsu, Seiichiro Katsura

Research output: Contribution to journalArticlepeer-review

Abstract

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 complicates the compensation. To eliminate the frequency-varying periodic disturbances, 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 paper 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 Levy flight. Moreover, the proposed method can reduce the number of the design parameters.

Original languageEnglish
JournalIEEE Transactions on Industrial Electronics
DOIs
Publication statusAccepted/In press - 2020

Keywords

  • Adaptive periodic-disturbance observer
  • Levy flight
  • differential evolutionary algorithm
  • memetic algorithm
  • periodic disturbance

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Electrical and Electronic Engineering

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