Abstract

In this work we examine the performance of iterative learning control (ILC) for systems with non-repeating disturbances and random noise. Single-input, single- output linear time-invariant systems and iteration-invariant learning filters are considered. We find that a tradeoff exists between the convergence rate and converged error spectrum. Optimal filter designs, which are dependant on the disturbance and noise spectra, are developed. We also present simple design guidelines for the case when explicit models of disturbance and noise spectra are not available. A numerical design example is presented.

Department(s)

Mechanical and Aerospace Engineering

Keywords and Phrases

Adaptive Control; Convergence of Numerical Methods; Frequency-Domain Analysis; Iterative Methodes; Learning Systems; Optimal Systems; Stochastic Systems

Document Type

Article - Conference proceedings

Document Version

Final Version

File Type

text

Language(s)

English

Rights

© 2008 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.

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