Research Papers

Output Information Based Iterative Learning Control Law Design With Experimental Verification

[+] Author and Article Information
Lukasz Hladowski, Eric Rogers

Krzysztof Galkowski

 Institute of Control and Computation Engineering, University of Zielona Gora, Podgorna 50, 65-246 Zielona Gora, PolandK.Galkowski@issi.uz.zgora.pl

Zhonglun Cai1

Chris T. Freeman, Paul L. Lewin

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


Corresponding author.

J. Dyn. Sys., Meas., Control 134(2), 021012 (Jan 12, 2012) (10 pages) doi:10.1115/1.4005038 History: Received August 03, 2009; Revised June 28, 2011; Published January 11, 2012; Online January 12, 2012

This paper considers iterative learning control law design using the theory of linear repetitive processes. This setting enables trial-to-trial error convergence and along-the-trial performance to be considered simultaneously in the design. It is also shown that this design extends naturally to include robustness to unmodeled plant dynamics. The results from experimental application of these laws to a gantry robot performing a pick and place operation are given, together with a discussion of the positioning of this approach relative to alternatives and possible further research.

Copyright © 2012 by American Society of Mechanical Engineers
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Figure 1

The gantry robot with the three axes marked

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Figure 2

X-axis Bode gain and phase plots where the dotted and dash lines represent the measured data, and the solid bold line corresponds to the transfer-function used in design

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Figure 3

The reference trajectories for the gantry robot

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Figure 4

The input, error, and output progression for the design with K1  = 0, K2  = 0, K3  = 100

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Figure 5

The output on trial ten (red line) compared to the reference (blue line) together with the input (middle plot), and the error (bottom plot)

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Figure 6

(a) No multisampling used; (b) multisampling for n∧=2

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Figure 7

Influence of the control law on the error ek (p)

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Figure 8

(a) Evolution of the output of X-axis for the first 20 trials in one experiment, (b) evolution of the control input, and (c) evolution of the error dynamics

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Figure 9

Along the trial performance on trial 200

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Figure 10

Experimentally measured input (left), output (center), and error (right) progression with trial number for Eq. 29

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Figure 11

Comparison of experimentally measured mean square error plotted against trial number for the control law matrices of Eqs. 29,30



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