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  • 标题:Input and plant parameter optimization via learning optimal control of Hamiltonian systems
  • 本地全文:下载
  • 作者:Satoshi Satoh ; Kenji Fujimoto ; Masami Saeki
  • 期刊名称:IFAC PapersOnLine
  • 印刷版ISSN:2405-8963
  • 出版年度:2015
  • 卷号:48
  • 期号:13
  • 页码:57-62
  • DOI:10.1016/j.ifacol.2015.10.214
  • 语种:English
  • 出版社:Elsevier
  • 摘要:AbstractThe proposed method solves a class of optimal control problems based on a symmetric property of Hamiltonian systems. It allows one to simultaneously obtain an optimal feedforward input and optimal plant parameters, which (at least locally) minimize a given cost function. The big advantage of this method is that the precise model of the dynamics of the plant system is not required by using the symmetric property of Hamiltonian systems, called variational symmetry. We also show some applications of this method to optimal gait generation for a one-legged hopping robot.
  • 关键词:KeywordsOptimal controlLearning controlRobot controlHamiltonian systemsGait generation
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