Skip to main navigation Skip to search Skip to main content

A kalman-filtering based iterative learning control algorithm

  • Harbin Institute of Technology

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

Abstract

A kalman filtering-based stochastic iterative learning control algorithm is proposed in this paper for linear stochastic systems with random noises. A learning gain matrix is designed by minimizing the trace of the mean-square matrix of the input tracking error. Theoretical results show that the proposed algorithm guarantees not only the asymptotic but also monotonic convergence of the input tracking error in the mean-square error sense under some basic probabilistic noise assumptions. Finally, a numerical example is included to illustrate the effectiveness of the proposed algorithms.

Original languageEnglish
Title of host publicationEuropean Society for Precision Engineering and Nanotechnology, Conference Proceedings - 19th International Conference and Exhibition, EUSPEN 2019
EditorsRichard K. Leach, D. Billington, C. Nisbet, D. Phillips
Publishereuspen
Pages596-599
Number of pages4
ISBN (Electronic)9780995775145
StatePublished - 2019
Event19th International Conference of the European Society for Precision Engineering and Nanotechnology, EUSPEN 2019 - Bilbao, Spain
Duration: 3 Jun 20197 Jun 2019

Publication series

NameEuropean Society for Precision Engineering and Nanotechnology, Conference Proceedings - 19th International Conference and Exhibition, EUSPEN 2019

Conference

Conference19th International Conference of the European Society for Precision Engineering and Nanotechnology, EUSPEN 2019
Country/TerritorySpain
CityBilbao
Period3/06/197/06/19

Keywords

  • Asymptotic convergence
  • Iterative learning control
  • Kalman-filter
  • Monotonic convergence
  • Stochastic noise

Fingerprint

Dive into the research topics of 'A kalman-filtering based iterative learning control algorithm'. Together they form a unique fingerprint.

Cite this