This paper examines robust stability and robust transient growth in Iterative Learning Control (ILC). It is well known that small perturbations in system dynamics can result in very large transient growth of some ILC systems. Even larger perturbations can result in instability. One ad hoc technique commonly employed to improve robustness is to slow the learning rate by reducing the learning filter gain or lowpass filtering the error signal. Here, pseudospectra analysis is used to analyze the robustness of ILC algorithms with slow learning. It is found that robustness bounds can be increased and transient growth decreased with decreasing learning gain. This result provides a new theoretical foundation for tuning approaches for improving robustness.
J. R. Singler and D. A. Bristow, "Robustness Analysis of Slow Learning in Iterative Learning Control Systems," Proceedings of the 2011 American Control Conference (2011: June 29 - July 1, San Francisco, CA), Institute of Electrical and Electronics Engineers (IEEE), Jan 2011.
The definitive version is available at https://doi.org/10.1109/ACC.2011.5991478
2011 American Control Conference (2011: June 29 - July 1, San Francisco, CA)
Mechanical and Aerospace Engineering
Mathematics and Statistics
Article - Conference proceedings
© 2011 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.