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    An extremum seeking approach to sampled-data iterative learning control of continuous-time non linear systems

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    Author
    Khong, SZ; Nesic, D; Krstic, M
    Date
    2016-12-22
    Source Title
    IFAC-PapersOnLine
    Publisher
    IFAC Secretariat
    University of Melbourne Author/s
    Nesic, Dragan
    Affiliation
    Electrical and Electronic Engineering
    Metadata
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    Document Type
    Journal Article
    Citations
    Khong, S. Z., Nesic, D. & Krstic, M. (2016). An extremum seeking approach to sampled-data iterative learning control of continuous-time non linear systems. IFAC-PapersOnLine, 49 (18), pp.962-967. https://doi.org/10.1016/j.ifacol.2016.10.292.
    Access Status
    Open Access
    URI
    http://hdl.handle.net/11343/251795
    DOI
    10.1016/j.ifacol.2016.10.292
    Abstract
    Iterative learning control (ILC) of continuous-time nonlinear plants with periodic sampled-data inputs is considered via an extremum seeking approach. ILC is performed without exploiting knowledge about any plant model, whereby the input signal is constructed recursively so that the corresponding plant output tracks a prescribed reference trajectory as closely as possible on a finite horizon. The ILC is formulated in terms of a non-model-based extremum seeking control problem, to which local optimisation methods such as gradient descent and Newton are applicable. Sufficient conditions on convergence to a neighbourhood of the reference trajectory are given.

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