Iterative learning control (ILC) is a learning technique used to improve the performance of systems that execute the same task multiple times. Learning transient behavior has emerged as an important topic in the design and analysis of ILC systems. In practice, the learning control is often low-pass filtered with a ldquoQ-filterrdquo to prevent transient growth, at the cost of performance. In this note, we consider linear time-invariant, discrete-time, single-input single-output systems, and convert frequency-domain uncertainty models to a time-domain representation for analysis. We then develop robust monotonic convergence conditions, which depend directly on the choice of the Q-filter and are independent of the nominal plant dynamics. This general result is then applied to a class of linear time-varying Q-filters that is particularly suited for precision motion control.
D. A. Bristow and A. G. Alleyne, "Monotonic Convergence of Iterative Learning Control for Uncertain Systems using a Time-Varying Filter," IEEE Transactions on Automatic Control, vol. 53, no. 2, pp. 582-585, Institute of Electrical and Electronics Engineers (IEEE), Mar 2008.
The definitive version is available at https://doi.org/10.1109/TAC.2007.914252
Mechanical and Aerospace Engineering
Keywords and Phrases
Adaptive Control; Convergence; Iterative Methods; Learning Systems; Time-Varying Filters; Uncertain Systems
International Standard Serial Number (ISSN)
Article - Journal
© 2008 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.