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  • 标题:Autonomous Cascade Structure Feedback Controller Design With Genetic Algorithm-based Structure Optimization
  • 本地全文:下载
  • 作者:Yoshihiro Maeda ; Shu Kunitate ; Eitaro Kuroda
  • 期刊名称:IFAC PapersOnLine
  • 印刷版ISSN:2405-8963
  • 出版年度:2020
  • 卷号:53
  • 期号:2
  • 页码:8419-8425
  • DOI:10.1016/j.ifacol.2020.12.1438
  • 语种:English
  • 出版社:Elsevier
  • 摘要:AbstractDesigning a precise feedback (FB) controller that realizes the required properties such as a wide bandwidth and robust stability/sensitivity for resonant modes is a key design issue in achieving the fast and precise positioning performance of galvano scanners used in laser drilling. The aim of this study is to develop an efficient controller structure optimization method in an autonomous cascade structure FB controller design. The genetic algorithm efficiently searches the optimal structure for a target plant with high-order resonant modes in a short time according to the fitness of the controller parameter optimization problem. The effectiveness of the proposed method is demonstrated through a comparison with the conventional full search-based structure optimization method using a laboratory galvano scanner.
  • 关键词:KeywordsAutonomous DesignFeedback ControlCascade StructureGain/phase StabilizationFastPrecise PositioningGalvano Scanner
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