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Research Papers

Discrete Fourier Transform Based Iterative Learning Control Design for Linear Plants With Experimental Verification

[+] Author and Article Information
C. T. Freeman, P. L. Lewin

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

E. Rogers

School of Electronics and Computer Science, University of Southampton, Southampton SO17 1BJ, UKetar@ecs.soton.ac.uk

D. H. Owens

Department of Automatic Control and Systems Engineering, University of Sheffield, Mappin Street, Sheffield S1 3JD, UK

J. J. Hatonen

 Borealis Polymers R&D, Porvoo FIN 06101, Finland; Control Engineering Laboratory, Helsinki University of Technology, Helsinki FI-02105 TKK, Finland

J. Dyn. Sys., Meas., Control 131(3), 031006 (Mar 19, 2009) (10 pages) doi:10.1115/1.3072149 History: Received September 17, 2007; Revised October 30, 2008; Published March 19, 2009

This paper considers the design of linear iterative learning control algorithms using the discrete Fourier transform of the measured impulse response of the system or plant under consideration. It is shown that this approach leads to a transparent design method whose performance is then experimentally benchmarked on an electromechanical system. The extension of this approach to the case when there is uncertainty associated with the systems under consideration is also addressed in both algorithm development and experimental benchmarking terms. The robustness results here have the applications oriented benefit of allowing the designer to manipulate the convergence and robustness properties of the algorithm in a straightforward manner.

Copyright © 2009 by American Society of Mechanical Engineers
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References

Figures

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Figure 1

Experimental test facility

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Figure 2

Experimentally obtained plant impulse response

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Figure 3

Permissible uncertainty in phase

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Figure 4

Tracking error using adjoint algorithm and sinewave demand

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Figure 5

Output tracking using the sinewave reference command and α=0.5

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Figure 6

Tracking error using adjoint algorithm and repeating sequence demand

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Figure 7

Output tracking using the repeating sequence reference command and ξ=6

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Figure 8

Geometry of the uncertainty space

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Figure 9

Examples of weighting function, W(x)

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Figure 10

Optimal solutions for (a) α>1 and (b) α<1

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Figure 11

Robustness plot for the adjoint algorithm

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Figure 12

Robustness plot for the inverse algorithm

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Figure 13

Bode plot using various rM functions and α=0.2

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Figure 14

Tracking error for the sinewave reference command

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Figure 15

Tracking error for the repeating sequence reference command

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Figure 16

Bode plot using various α, and ξ=6

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Figure 17

Tracking error for the sinewave reference command

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Figure 18

Tracking error for the repeating sequence reference command

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