Kalman filter based iterative learning control for discrete time MIMO systems

Rangana N. Jayawardhana, Bijoy K. Ghosh

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Scopus citations

Abstract

A new iterative learning control (ILC) algorithm for linear, time invariant, multi-input multi-output, dynamical systems in discrete time is introduced. The gain of the well known P-type ILC algorithm is updated, in every iteration, by solving a discrete Riccati equation that requires the knowledge of the first Markov parameter matrix CB of the dynamical system. The proposed Kalman Filter based learning algorithm is implementable block-wise which is similar to the, recently introduced, Luenberger observer based ILC algorithm.

Original languageEnglish
Title of host publicationProceedings of the 30th Chinese Control and Decision Conference, CCDC 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2257-2264
Number of pages8
ISBN (Electronic)9781538612439
DOIs
StatePublished - Jul 6 2018
Event30th Chinese Control and Decision Conference, CCDC 2018 - Shenyang, China
Duration: Jun 9 2018Jun 11 2018

Publication series

NameProceedings of the 30th Chinese Control and Decision Conference, CCDC 2018

Conference

Conference30th Chinese Control and Decision Conference, CCDC 2018
CountryChina
CityShenyang
Period06/9/1806/11/18

Keywords

  • Kalman filter
  • block implementation
  • iterative learning control

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