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    Iterative learning control for manipulator trajectory tracking without any control singularity

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    Publication date
    2002
    Author
    Jiang, Ping
    Woo, P.
    Unbehauen, R.
    Keyword
    Visual servo
    Adaptive control
    Singularity avoidance
    Iterative learning control
    Peer-Reviewed
    Yes
    
    Metadata
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    Abstract
    In this paper, we investigate trajectory tracking in a multi-input nonlinear system, where there is little knowledge of the system parameters and the form of the nonlinear function. An identification-based iterative learning control (ILC) scheme to repetitively estimate the linearity in a neighborhood of a desired trajectory is presented. Based on this estimation, the original nonlinear system can track the desired trajectory perfectly by the aid of a regional training scheme. Just like in adaptive control, a singularity exists in ILC when the input coupling matrix is estimated. Singularity avoidance is discussed. A new parameter modification procedure for ILC is presented such that the determinant of the estimate of the input coupling matrix is uniformly bounded from below. Compared with the scheme used for adaptive control of a MIMO system, the proposed scheme reduces the computation load greatly. It is used in a robotic visual system for manipulator trajectory tracking without any information about the camera-robot relationship. The estimated image Jacobian is updated repetitively and then its inverse is used to calculate the manipulator velocity without any singularity.
    URI
    http://hdl.handle.net/10454/3158
    Version
    No full-text available in the repository
    Citation
    Jiang, P., Woo, P. and Unbehauen, R. (2002). Iterative learning control for manipulator trajectory tracking without any control singularity. Robotica. Vol. 20, No. 2, pp. 149-158.
    Link to publisher’s version
    http://dx.doi.org/10.1017/S026357470100368X
    Type
    Article
    Collections
    Engineering and Informatics Publications

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