Iterative learning control of iteration-varying systems via robust update laws with experimental implementation

B. Altın; J. Willems; T.A.E. Oomen; K. Barton

doi:10.1016/j.conengprac.2017.02.005

Iterative learning control of iteration-varying systems via robust update laws with experimental implementation

B. Altın, J. Willems, T.A.E. Oomen, K. Barton

Control Systems Technology

Research output: Contribution to journal › Article › Academic › peer-review

15 Citations (Scopus)

4 Downloads (Pure)

Abstract

Iterative learning control (ILC) is an efficient way of improving the tracking performance of repetitive systems. While ILC can offer significant improvement to the transient response of complex dynamical systems, the fundamental assumption of iteration invariance of the process limits potential applications. Utilizing abstract Banach spaces as our problem setting, we develop a general approach that is applicable to the various frameworks encountered in ILC. Our main result is that robust invariant update laws lead to stable behavior in ILC systems, where iteration-varying systems converge to bounded neighborhoods of their nominal counterparts when uncertainties are bounded. Furthermore, if the uncertainties are convergent along the iteration axis, convergence to the nominal case can be guaranteed.

Original language	English
Pages (from-to)	36-45
Number of pages	10
Journal	Control Engineering Practice
Volume	62
DOIs	https://doi.org/10.1016/j.conengprac.2017.02.005
Publication status	Published - 1 May 2017

Keywords

Iterative methods
Learning control
Recursive control algorithms
Robustness
Uncertainty

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10.1016/j.conengprac.2017.02.005

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Altın, B., Willems, J., Oomen, T. A. E., & Barton, K. (2017). Iterative learning control of iteration-varying systems via robust update laws with experimental implementation. Control Engineering Practice, 62, 36-45. https://doi.org/10.1016/j.conengprac.2017.02.005

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abstract = "Iterative learning control (ILC) is an efficient way of improving the tracking performance of repetitive systems. While ILC can offer significant improvement to the transient response of complex dynamical systems, the fundamental assumption of iteration invariance of the process limits potential applications. Utilizing abstract Banach spaces as our problem setting, we develop a general approach that is applicable to the various frameworks encountered in ILC. Our main result is that robust invariant update laws lead to stable behavior in ILC systems, where iteration-varying systems converge to bounded neighborhoods of their nominal counterparts when uncertainties are bounded. Furthermore, if the uncertainties are convergent along the iteration axis, convergence to the nominal case can be guaranteed.",

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