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    Iterative learning control with unknown control direction: a novel data-based approach

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    Authors
    Shen, Dong
    Hou, Zhongsheng
    Affiliation
    Chinese Academy of Sciences
    Issue Date
    2011
    Subjects
    iterative learning control (ILC)
    iterative learning control
    
    Metadata
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    Abstract
    Iterative learning control (ILC) is considered for both deterministic and stochastic systems with unknown control direction. To deal with the unknown control direction, a novel switching mechanism, based only on available system tracking error data, is first proposed. Then two ILC algorithms combined with the novel switching mechanism are designed for both deterministic and stochastic systems. It is proved that the ILC algorithms would switch to the right control direction and stick to it after a finite number of cycles. Moreover, the input sequence converges to the desired one under the deterministic case. The input sequence converges to the optimal one with probability 1 under stochastic case and the resulting tracking error tends to its minimal value.
    Citation
    Shen, D. and Hou, Z. (2011) 'Iterative Learning Control With Unknown Control Direction: A Novel Data-Based Approach' IEEE Transactions on Neural Networks 22 (12):2237-2249
    Publisher
    IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
    Journal
    IEEE Transactions on Neural Networks
    URI
    http://hdl.handle.net/10547/275872
    DOI
    10.1109/TNN.2011.2175947
    Additional Links
    http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6087286
    Type
    Article
    Language
    en
    ISSN
    1045-9227
    1941-0093
    ae974a485f413a2113503eed53cd6c53
    10.1109/TNN.2011.2175947
    Scopus Count
    Collections
    Centre for Research in Distributed Technologies (CREDIT)

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