Title

Monotonic Convergence of Iterative Learning Control for Uncertain Systems using a Time-Varying Q-Filter

Abstract

Time-varying Q-filtering in iterative learning control (ILC) has demonstrated potential performance benefits over time-invariant Q-filtering. In this paper, LTV Q-filtering of ILC is considered for uncertain systems. Sufficient conditions for stability and the important monotonic convergence property are developed for the uncertain system. A class of LTV Q-filters that has particular benefit for rapid motion trajectories is presented, and monotonic convergence conditions are developed. The developed conditions highlight a relationship that the bandwidth can be increased locally and decreased elsewhere to localize high performance at specific times. These conditions are also iteration-length invariant and allow for significant design freedom after analysis enabling online modification of the LTV Q-filter.

Meeting Name

American Control Conference (2005: Jun. 8-10, Portland, OR)

Department(s)

Mechanical and Aerospace Engineering

Keywords and Phrases

Convergence; Control systems; Uncertain systems; Time varying systems; Stability; Bandwidth; Robustness; Frequency; TV; Industrial engineering

International Standard Serial Number (ISSN)

0743-1619

Document Type

Article - Conference proceedings

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2005 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.

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