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.
D. A. Bristow, "Frequency Domain Analysis and Design of Iterative Learning Control for Systems with Stochastic Disturbances," American Control Conference, Institute of Electrical and Electronics Engineers (IEEE), Jun 2008.
The definitive version is available at http://dx.doi.org/10.1109/ACC.2008.4587102
Mechanical and Aerospace Engineering
Keywords and Phrases
Adaptive Control; Convergence of Numerical Methods; Frequency-Domain Analysis; Iterative Methodes; Learning Systems; Optimal Systems; Stochastic Systems
Article - Conference proceedings
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