首页    期刊浏览 2024年11月28日 星期四
登录注册

文章基本信息

  • 标题:Design of a Data-Driven Control System using a Multi-Objective Genetic Algorithm
  • 本地全文:下载
  • 作者:Takuya Kinoshita ; Toru Yamamoto
  • 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:310-313
  • DOI:10.1016/j.ifacol.2019.12.668
  • 语种:English
  • 出版社:Elsevier
  • 摘要:In recent years, control design schemes for directly calculating control parameters from operational data have been realized and include the virtual reference feedback tuning (VRFT) method and the fictitious reference iterative tuning (FRIT) method. They were designed for objects that have a linear system. However, many objects in industry are nonlinear; hence, it is challenging to obtain good control performance by only applying fixed PID controllers. In this study, multiple linear systems as objects using multiple linear controllers are investigated. Specifically, it is necessary to solve two optimization problems of (i) the number of controllers (ii) the control parameters of each controller, and it is solving by using multi-objective genetic algorithm (MOGA) in this research.
  • 关键词:KeywordsMulti-objective genetic algorithmdata-driven controlVRFT
国家哲学社会科学文献中心版权所有