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  • 标题:Study on a Sub-databases-driven (S-DD) Controller using k-means Clustering
  • 本地全文:下载
  • 作者:Shin Wakitani ; Hiroki Nakanishi ; Toru Yamamoto
  • 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:212-217
  • DOI:10.1016/j.ifacol.2020.12.124
  • 语种:English
  • 出版社:Elsevier
  • 摘要:AbstractA database-driven PID (DD-PID) control method is one of the effective control methods for nonlinear systems. In the conventional DD-PID control method, there is a problem that the calculation cost and required memory for creating an optimal database are large. For the above problem, this paper proposes a method to implement the DD-PID controller with small-sized sub-databases. In the proposed method, one database that includes past I/O data and PID gains are created, and the database is updated in an offline manner. Moreover, sub-databases are constructed by clustering the created database using the k-means clustering method. The number of clusters for k-means clustering is determined automatically based on kernel functions. The effectiveness of the proposed method is presented by numerical examples.
  • 关键词:KeywordsPID controlLearning controlDatabase management systemsLearning algorithmsNonlinear control
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