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    A Hybrid Time-Scaling Transformation for Time-Delay Optimal Control Problems

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    Access Status
    Open access
    Authors
    Yu, Changjun
    Lin, Qun
    Loxton, R.
    Teo, Kok Lay
    Wang, G.
    Date
    2015
    Type
    Journal Article
    
    Metadata
    Show full item record
    Citation
    Yu, C. and Lin, Q. and Loxton, R. and Teo, K.L. and Wang, G. 2015. A Hybrid Time-Scaling Transformation for Time-Delay Optimal Control Problems. Journal of Optimization Theory and Applications. 169 (3): pp. 876–901.
    Source Title
    Journal of Optimization Theory and Applications
    DOI
    10.1007/s10957-015-0783-z
    ISSN
    0022-3239
    School
    Department of Mathematics and Statistics
    URI
    http://hdl.handle.net/20.500.11937/19783
    Collection
    • Curtin Research Publications
    Abstract

    In this paper, we consider a class of nonlinear time-delay optimal control problems with canonical equality and inequality constraints. We propose a new computational approach, which combines the control parameterization technique with a hybrid time-scaling strategy, for solving this class of optimal control problems. The proposed approach involves approximating the control variables by piecewise constant functions, whose heights and switching times are decision variables to be optimized. Then, the resulting problem with varying switching times is transformed, via a new hybrid time-scaling strategy, into an equivalent problem with fixed switching times, which is much preferred for numerical computation. Our new time-scaling strategy is hybrid in the sense that it is related to two coupled time-delay systems—one defined on the original time scale, in which the switching times are variable, the other defined on the new time scale, in which the switching times are fixed. This is different from the conventional time-scaling transformation widely used in the literature, which is not applicable to systems with time-delays. To demonstrate the effectiveness of the proposed approach, we solve four numerical examples. The results show that the costs obtained by our new approach are lower, when compared with those obtained by existing optimal control methods.

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