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    Adaptive Iterative Learning Control for Nonlinear Systems with Unknown Control Gain

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    Publication date
    2004
    Author
    Jiang, Ping
    Chen, H.
    Keyword
    Adaptive Control
    Convergence
    Nonlinear Control Systems
    Learning Systems
    Uncertain Systems
    Nonlinear Dynamical Systems
    Iterative Methods
    Control System Synthesis
    Peer-Reviewed
    Yes
    
    Metadata
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    Abstract
    An adaptive iterative learning control approach is proposed for a class of single-input single-output uncertain nonlinear systems with completely unknown control gain. Unlike the ordinary iterative learning controls that require some preconditions on the learning gain to stabilize the dynamic systems, the adaptive iterative learning control achieves the convergence through a learning gain in a Nussbaum-type function for the unknown control gain estimation. This paper shows that all tracking errors along a desired trajectory in a finite time interval can converge into any given precision through repetitive tracking. Simulations are carried out to show the validity of the proposed control method.
    URI
    http://hdl.handle.net/10454/4154
    Version
    No full-text available in the repository
    Citation
    Jian, P. and Chen, H. (2004). Adaptive Iterative Learning Control for Nonlinear Systems with Unknown Control Gain. Journal of Dynamic Systems Measurement and Control. Vol. 126, No. 4, pp. 916-921.
    Link to publisher’s version
    http://dx.doi.org/10.1115/1.1850538
    Type
    Article
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    Engineering and Informatics Publications

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