An efficient observer-based ILC for time-varying nonlinear systems

Jason Sheng-Hon Tsai, Chia Hsing Chen, Shu-Mei Guo, Leang San Shieh

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

Abstract

In this paper, an observer-based iterative learning control (OBILC) with linear quadratic regulator (LQR) observer is proposed for the tracking problem of a class of time-varying nonlinear systems, where the state information is not available. The proposed observer-based tracker yields a good tracking performance both in transient and steady state response. By linearizing the nonlinear system, we construct the state observer, based on the linear quadratic analog tracker (LQAT) design technique, with a high gain property first. Thereafter, we propose the iterative learning law, based on the LQR observer, ensuring the boundedness of the tracking error. Moreover, it is shown that if the initial state variables are known, a perfect convergence to zero, over a finite tracking horizon, can be obtained, when the number of iterations tends to infinity.

Original languageEnglish
Title of host publicationProceedings of the 28th IASTED International Conference on Modelling, Identification, and Control, MIC 2009
Pages254-259
Number of pages6
Publication statusPublished - 2009 Dec 1
Event28th IASTED International Conference on Modelling, Identification, and Control, MIC 2009 - Innsbruck, Austria
Duration: 2009 Feb 162009 Feb 18

Publication series

NameProceedings of the IASTED International Conference on Modelling, Identification, and Control, MIC
ISSN (Print)1025-8973

Other

Other28th IASTED International Conference on Modelling, Identification, and Control, MIC 2009
CountryAustria
CityInnsbruck
Period09-02-1609-02-18

All Science Journal Classification (ASJC) codes

  • Software
  • Modelling and Simulation
  • Computer Science Applications

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