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Robust monotone gradient-based discrete-time iterative learning control

Robust monotone gradient-based discrete-time iterative learning control
Robust monotone gradient-based discrete-time iterative learning control
This paper considers the use of matrix models and the robustness of a gradient-based iterative learning control (ILC) algorithm using both fixed learning gains and nonlinear data-dependent gains derived from parameter optimization. The philosophy of the paper is to ensure monotonic convergence with respect to the mean-square value of the error time series. The paper provides a complete and rigorous analysis for the systematic use of the well-known matrix models in ILC. Matrix models provide necessary and sufficient conditions for robust monotonic convergence. They also permit the construction of accurate sufficient frequency domain conditions for robust monotonic convergence on finite time intervals for both causal and non-causal controller dynamics. The results are compared with recently published results for robust inverse-model-based ILC algorithms and it is seen that the algorithm has the potential to improve the robustness to high-frequency modelling errors, provided that resonances within the plant bandwidth have been suppressed by feedback or series compensation.
iterative learning control, robust control, parameter optimization, =, positive-real systems
1049-8923
634-661
Owens, D.H.
db24b8ef-282b-47c0-9cd2-75e91d312ad7
Hatonen, J.J.
0a092c0d-ec99-42d5-b295-217fb85ef014
Daley, S.
53cef7f1-77fa-4a4c-9745-b6a0ba4f42e6
Owens, D.H.
db24b8ef-282b-47c0-9cd2-75e91d312ad7
Hatonen, J.J.
0a092c0d-ec99-42d5-b295-217fb85ef014
Daley, S.
53cef7f1-77fa-4a4c-9745-b6a0ba4f42e6

Owens, D.H., Hatonen, J.J. and Daley, S. (2009) Robust monotone gradient-based discrete-time iterative learning control. International Journal of Robust and Nonlinear Control, 19 (6), 634-661. (doi:10.1002/rnc.1338).

Record type: Article

Abstract

This paper considers the use of matrix models and the robustness of a gradient-based iterative learning control (ILC) algorithm using both fixed learning gains and nonlinear data-dependent gains derived from parameter optimization. The philosophy of the paper is to ensure monotonic convergence with respect to the mean-square value of the error time series. The paper provides a complete and rigorous analysis for the systematic use of the well-known matrix models in ILC. Matrix models provide necessary and sufficient conditions for robust monotonic convergence. They also permit the construction of accurate sufficient frequency domain conditions for robust monotonic convergence on finite time intervals for both causal and non-causal controller dynamics. The results are compared with recently published results for robust inverse-model-based ILC algorithms and it is seen that the algorithm has the potential to improve the robustness to high-frequency modelling errors, provided that resonances within the plant bandwidth have been suppressed by feedback or series compensation.

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Published date: 10 April 2009
Keywords: iterative learning control, robust control, parameter optimization, =, positive-real systems
Organisations: Signal Processing & Control Group

Identifiers

Local EPrints ID: 186755
URI: http://eprints.soton.ac.uk/id/eprint/186755
ISSN: 1049-8923
PURE UUID: 1e5bc909-42e1-4885-9147-45fe8d8d8ab2

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Date deposited: 16 May 2011 08:54
Last modified: 26 Oct 2023 17:36

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Contributors

Author: D.H. Owens
Author: J.J. Hatonen
Author: S. Daley

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