Intermittent sampling in iterative learning control: a monotonically-convergent gradient-descent approach with application to time stamping

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The standard assumption that a measurement signal is available at each sample in iterative learning control (ILC) is not always justified, e.g., in systems with data dropouts or when exploiting time-stamped data from incremental encoders. The aim of this paper is to develop a computationally tractable ILC framework for systems with arbitrary time- varying measurement points. New conditions for monotonic convergence of the input signal are established. These lead to a new single centralized design approach independent of the sampling times reminiscent of gradient-descent ILC. The approach is demonstrated in a simulation example of a massspring-damper system from which exact time-varying time- stamped data from the incremental encoder is available.

Originele taal-2Engels
Titel2019 IEEE 58th Conference on Decision and Control (CDC)
Plaats van productiePiscataway
UitgeverijInstitute of Electrical and Electronics Engineers
Pagina's6542-6547
Aantal pagina's6
ISBN van elektronische versie978-1-7281-1397-5
DOI's
StatusGepubliceerd - 2019
Evenement58th IEEE Conference on Decision and Control (CDC 2019) - Nice, Frankrijk
Duur: 11 dec 201913 dec 2019

Congres

Congres58th IEEE Conference on Decision and Control (CDC 2019)
Verkorte titelCDC 2019
LandFrankrijk
StadNice
Periode11/12/1913/12/19

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  • Citeer dit

    Strijbosch, N., & Oomen, T. A. E. (2019). Intermittent sampling in iterative learning control: a monotonically-convergent gradient-descent approach with application to time stamping. In 2019 IEEE 58th Conference on Decision and Control (CDC) (blz. 6542-6547). [9029312] Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/CDC40024.2019.9029312