Monotonic Convergence of Iterative Learning Control for Uncertain Systems using a Time-Varying Q-Filter
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.
D. A. Bristow and A. G. Alleyne, "Monotonic Convergence of Iterative Learning Control for Uncertain Systems using a Time-Varying Q-Filter," Proceedings of the American Control Conference (2005, Portland, OR), Institute of Electrical and Electronics Engineers (IEEE), Jan 2005.
The definitive version is available at https://doi.org/10.1109/ACC.2005.1469927
American Control Conference (2005: Jun. 8-10, Portland, OR)
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)
Article - Conference proceedings
© 2005 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.
01 Jan 2005