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  • 标题:A New Approach of Data-Driven Controller Tuning Method By Using Virtual IMC Structure—Virtual Internal Model Tuning— ⁎
  • 本地全文:下载
  • 作者:Taichi Ikezaki ; Osamu Kaneko
  • 期刊名称:IFAC PapersOnLine
  • 印刷版ISSN:2405-8963
  • 出版年度:2019
  • 卷号:52
  • 期号:29
  • 页码:344-349
  • DOI:10.1016/j.ifacol.2019.12.699
  • 语种:English
  • 出版社:Elsevier
  • 摘要:In this paper, we propose a new method for updating controllers by using closed loop data without a mathematical model of plant. The proposed method here requires only one-shot output data obtained in the closed loop experiment together with the feedback controller used in this initial experiment and the desired tracking transfer function. This method is derived based on the idea of the data-driven prediction and the concept of the internal model control (IMC) structure that only appears in the virtual closed loop system. Finally, the validity and the effectiveness of the proposed method are verified through experimental examples.
  • 关键词:KeywordsClosed-loop controllerData-Driven ControlInternal Model ControlVirtual Internal Model (VIMT)
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