On the use of noncausal LTI operators in iterative learning control

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    Abstract

    This paper demonstrates the use of noncausal operators in iterative learning control (ILC). First, it is shown that for a particular class of plants (having unstable zeros), perfect tracking can only be achieved by using noncausal operators. Then it is shown that with any converging algorithm (both causal and noncausal) we can associate a particular feedback controller. For causal algorithms this controller can be shown to be internally stabilizing. In the noncausal case, however, the associated controller is found to be generally destabilizing which implies that the existing notion of an equivalent controller for causal ILC does not extend to noncausal ILC.
    Original languageEnglish
    Title of host publicationProceedings of the 41st IEEE Conference on Decision and Control
    Place of PublicationLas Vegas, Nevada (USA)
    PublisherIEEE
    Pages3362-3366
    Number of pages5
    ISBN (Print)0-7803-7516-5
    DOIs
    Publication statusPublished - 10 Dec 2002
    Event41st IEEE Conference on Decision and Control, CDC 2002 - Las Vegas, United States
    Duration: 10 Dec 200213 Dec 2002
    Conference number: 41

    Publication series

    Name
    PublisherIEEE
    Volume3

    Conference

    Conference41st IEEE Conference on Decision and Control, CDC 2002
    Abbreviated titleCDC
    CountryUnited States
    CityLas Vegas
    Period10/12/0213/12/02

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    Keywords

    • METIS-210853
    • IR-43865

    Cite this

    Verwoerd, M. H. A., Meinsma, G., & de Vries, T. J. A. (2002). On the use of noncausal LTI operators in iterative learning control. In Proceedings of the 41st IEEE Conference on Decision and Control (pp. 3362-3366). Las Vegas, Nevada (USA): IEEE. https://doi.org/10.1109/CDC.2002.1184394
    Verwoerd, M.H.A. ; Meinsma, Gjerrit ; de Vries, Theodorus J.A. / On the use of noncausal LTI operators in iterative learning control. Proceedings of the 41st IEEE Conference on Decision and Control. Las Vegas, Nevada (USA) : IEEE, 2002. pp. 3362-3366
    @inproceedings{a38b2f507c6249879bc915bef4dfc5fc,
    title = "On the use of noncausal LTI operators in iterative learning control",
    abstract = "This paper demonstrates the use of noncausal operators in iterative learning control (ILC). First, it is shown that for a particular class of plants (having unstable zeros), perfect tracking can only be achieved by using noncausal operators. Then it is shown that with any converging algorithm (both causal and noncausal) we can associate a particular feedback controller. For causal algorithms this controller can be shown to be internally stabilizing. In the noncausal case, however, the associated controller is found to be generally destabilizing which implies that the existing notion of an equivalent controller for causal ILC does not extend to noncausal ILC.",
    keywords = "METIS-210853, IR-43865",
    author = "M.H.A. Verwoerd and Gjerrit Meinsma and {de Vries}, {Theodorus J.A.}",
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    language = "English",
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    Verwoerd, MHA, Meinsma, G & de Vries, TJA 2002, On the use of noncausal LTI operators in iterative learning control. in Proceedings of the 41st IEEE Conference on Decision and Control. IEEE, Las Vegas, Nevada (USA), pp. 3362-3366, 41st IEEE Conference on Decision and Control, CDC 2002, Las Vegas, United States, 10/12/02. https://doi.org/10.1109/CDC.2002.1184394

    On the use of noncausal LTI operators in iterative learning control. / Verwoerd, M.H.A.; Meinsma, Gjerrit; de Vries, Theodorus J.A.

    Proceedings of the 41st IEEE Conference on Decision and Control. Las Vegas, Nevada (USA) : IEEE, 2002. p. 3362-3366.

    Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

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    Y1 - 2002/12/10

    N2 - This paper demonstrates the use of noncausal operators in iterative learning control (ILC). First, it is shown that for a particular class of plants (having unstable zeros), perfect tracking can only be achieved by using noncausal operators. Then it is shown that with any converging algorithm (both causal and noncausal) we can associate a particular feedback controller. For causal algorithms this controller can be shown to be internally stabilizing. In the noncausal case, however, the associated controller is found to be generally destabilizing which implies that the existing notion of an equivalent controller for causal ILC does not extend to noncausal ILC.

    AB - This paper demonstrates the use of noncausal operators in iterative learning control (ILC). First, it is shown that for a particular class of plants (having unstable zeros), perfect tracking can only be achieved by using noncausal operators. Then it is shown that with any converging algorithm (both causal and noncausal) we can associate a particular feedback controller. For causal algorithms this controller can be shown to be internally stabilizing. In the noncausal case, however, the associated controller is found to be generally destabilizing which implies that the existing notion of an equivalent controller for causal ILC does not extend to noncausal ILC.

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    Verwoerd MHA, Meinsma G, de Vries TJA. On the use of noncausal LTI operators in iterative learning control. In Proceedings of the 41st IEEE Conference on Decision and Control. Las Vegas, Nevada (USA): IEEE. 2002. p. 3362-3366 https://doi.org/10.1109/CDC.2002.1184394