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  • 标题:Data-driven Linear Quadratic Regulation via Semidefinite Programming
  • 本地全文:下载
  • 作者:Monica Rotulo ; Claudio De Persis ; Pietro Tesi
  • 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:3995-4000
  • DOI:10.1016/j.ifacol.2020.12.2264
  • 语种:English
  • 出版社:Elsevier
  • 摘要:AbstractThis paper studies the finite-horizon linear quadratic regulation problem where the dynamics of the system are assumed to be unknown and the state is accessible. Information on the system is given by a finite set of input-state data, where the input injected in the system is persistently exciting of a sufficiently high order. Using data, the optimal control law is then obtained as the solution of a suitable semidefinite program. The effectiveness of the approach is illustrated via numerical examples.
  • 关键词:KeywordsData-driven controlLinear quadratic regulationSemidefinite programming
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