Multiresolution finite-dimensional adaptive control of distributed parameter systems

Jun Y. Kim, Joseph Bentsman

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

Abstract

A multiresolution-based technique is proposed for the flnite- dimensionalization of the controller parameter adaptation laws in adaptive control of distributed parameter systems (DPS) with spatially-varying coefficients. This technique permits efficient incorporation of the prior knowledge of the specific plant parameter characteristics, such as nonsmoothness, into controller implementation through the choice of parameter approximation basis, yielding a high fidelity parameter representation by a small number of basis coefficients. For this purpose, a new tool - the multiresolution Lyapunov functional is introduced. Using the latter, the existence of the wavelet-based finite-dimensional parameter adaptation law providing the desired tracking accuracy, while retaining the well-posedness of the closed-loop system with the infinite-dimensional plant is proven. The benefits of the technique in both real-time and off-line performance enhancement of the control law, such as reduction of computational demand and increase in the output convergence rate unaccompanied by the corresponding increase in the control effort are demonstrated, as well.

Original languageEnglish (US)
Title of host publicationProceedings of the 45th IEEE Conference on Decision and Control 2006, CDC
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2801-2806
Number of pages6
ISBN (Print)1424401712, 9781424401710
DOIs
StatePublished - 2006
Event45th IEEE Conference on Decision and Control 2006, CDC - San Diego, CA, United States
Duration: Dec 13 2006Dec 15 2006

Publication series

NameProceedings of the IEEE Conference on Decision and Control
ISSN (Print)0191-2216

Other

Other45th IEEE Conference on Decision and Control 2006, CDC
Country/TerritoryUnited States
CitySan Diego, CA
Period12/13/0612/15/06

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Modeling and Simulation
  • Control and Optimization

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