Abstract

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.

Meeting Name

2011 American Control Conference (2011: June 29 - July 1, San Francisco, CA)

Department(s)

Mechanical and Aerospace Engineering

Second Department

Mathematics and Statistics

Document Type

Article - Conference proceedings

Document Version

Final Version

File Type

text

Language(s)

English

Rights

© 2011 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.

Share

 
COinS