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  • 标题:Efficient Train Operation via Shrinking Horizon Parametrized Predictive Control
  • 本地全文:下载
  • 作者:Hafsa Farooqi ; Lorenzo Fagiano ; Patrizio Colaneri
  • 期刊名称:IFAC PapersOnLine
  • 印刷版ISSN:2405-8963
  • 出版年度:2018
  • 卷号:51
  • 期号:20
  • 页码:203-208
  • DOI:10.1016/j.ifacol.2018.11.014
  • 语种:English
  • 出版社:Elsevier
  • 摘要:AbstractThe problem of driver assistance for the energy-efficient operation of trains is considered. The goal is to control the traction/braking forces applied to the train, while satisfying speed limits and reaching the next station at the prescribed arrival time. Moreover, the control input has to belong to a discrete set of values and/or operating modes, which a human driver has to implement. A nonlinear model predictive control (MPC) approach is proposed, featuring a shrinking horizon and an input-parametrization strategy to retain a continuous optimization problem. Theoretical convergence guarantees are derived, and the approach is tested in realistic simulations.
  • 关键词:KeywordsModel Predictive ControlInput ParametrizationNonlinear control systemsTrain control
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