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

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Abstract

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.

Original languageEnglish
Title of host publication2019 IEEE 58th Conference on Decision and Control (CDC)
Place of PublicationPiscataway
PublisherInstitute of Electrical and Electronics Engineers
Pages6542-6547
Number of pages6
ISBN (Electronic)978-1-7281-1397-5
DOIs
Publication statusPublished - 2019
Event58th IEEE Conference on Decision and Control (CDC 2019) - Nice, France
Duration: 11 Dec 201913 Dec 2019
https://cdc2019.ieeecss.org/

Conference

Conference58th IEEE Conference on Decision and Control (CDC 2019)
Abbreviated titleCDC 2019
CountryFrance
CityNice
Period11/12/1913/12/19
Internet address

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