On the Instant Iterative Learning MPC for Nonlinear Systems

Kaito Sato, Kenji Sawada, Masaki Inoue

研究成果: Conference contribution

抄録

Model predictive control (MPC) is one of the methods which optimizes the trajectory of the system with the constraints from predicted states of the system. A number of researches have studied its applications, for example, online optimization methods and fast solvers for nonlinear systems, because of its effectiveness. We propose one of the methods to apply online MPC to nonlinear systems based on instant MPC (iMPC). We recast iterative learning MPC (ILMPC) for nonlinear systems as iMPC via the primal-dual gradient algorithm, which we name "i-ILMPC". Finally, a numerical simulation is performed to demonstrate its effectiveness.

本文言語English
ホスト出版物のタイトル2020 59th Annual Conference of the Society of Instrument and Control Engineers of Japan, SICE 2020
出版社Institute of Electrical and Electronics Engineers Inc.
ページ1166-1171
ページ数6
ISBN(電子版)9781728110899
出版ステータスPublished - 2020 9 23
イベント59th Annual Conference of the Society of Instrument and Control Engineers of Japan, SICE 2020 - Chiang Mai, Thailand
継続期間: 2020 9 232020 9 26

出版物シリーズ

名前2020 59th Annual Conference of the Society of Instrument and Control Engineers of Japan, SICE 2020

Conference

Conference59th Annual Conference of the Society of Instrument and Control Engineers of Japan, SICE 2020
CountryThailand
CityChiang Mai
Period20/9/2320/9/26

ASJC Scopus subject areas

  • Control and Optimization
  • Instrumentation
  • Computer Vision and Pattern Recognition
  • Signal Processing
  • Decision Sciences (miscellaneous)
  • Industrial and Manufacturing Engineering

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