On the Instant Iterative Learning MPC for Nonlinear Systems

Kaito Sato, Kenji Sawada, Masaki Inoue

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish
Title of host publication2020 59th Annual Conference of the Society of Instrument and Control Engineers of Japan, SICE 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1166-1171
Number of pages6
ISBN (Electronic)9781728110899
Publication statusPublished - 2020 Sep 23
Event59th Annual Conference of the Society of Instrument and Control Engineers of Japan, SICE 2020 - Chiang Mai, Thailand
Duration: 2020 Sep 232020 Sep 26

Publication series

Name2020 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

Keywords

  • Iterative learning control
  • Model predictive control
  • continuous optimization

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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