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Technical Briefs

Initial Input Selection for Iterative Learning Control

[+] Author and Article Information
Chris T. Freeman1

Muhammad Ali Alsubaie, Zhonglun Cai, Eric Rogers, Paul L. Lewin

School of Electronics and Computer Science,  University of Southampton, Southampton SO17 1BJ, UK

1

Corresponding author.

J. Dyn. Sys., Meas., Control 133(5), 054504 (Aug 05, 2011) (6 pages) doi:10.1115/1.4003096 History: Received July 10, 2009; Revised July 23, 2010; Published August 05, 2011; Online August 05, 2011

Iterative learning control algorithms have been shown to offer a high level of performance both theoretically and when applied to practical applications. However, the trial-to-trial convergence of the error is generally highly dependent on the initial choice of input applied to the plant. Techniques are therefore developed, which generate an optimal initial input selection, and the effect this has on the error over subsequent trials is examined. Experimental benchmarking is undertaken using a gantry robot test facility.

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Copyright © 2011 by American Society of Mechanical Engineers
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Figures

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Figure 1

X-axis Bode plot

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Figure 2

Set of three stored references for experience-based input selection

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Figure 3

Effect of experience-based initial input selection on the X-axis: (a) input, (b) output, and (c) error during second trial

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Figure 4

X-axis error evolution using experience-based initial input selection

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Figure 5

Hybrid approach with Q=1,3 for (a) M=1, (b) M=2, and (c) M=3

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