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    Optimal train control via switched system dynamic optimization

    89310.pdf (614.8Kb)
    Access Status
    Open access
    Authors
    Zhong, W.
    Lin, Qun
    Loxton, Ryan
    Lay Teo, Kok
    Date
    2021
    Type
    Journal Article
    
    Metadata
    Show full item record
    Citation
    Zhong, W. and Lin, Q. and Loxton, R. and Lay Teo, K. 2021. Optimal train control via switched system dynamic optimization. Optimization Methods and Software. 36 (2-3): pp. 602-626.
    Source Title
    Optimization Methods and Software
    DOI
    10.1080/10556788.2019.1604704
    ISSN
    1055-6788
    Faculty
    Faculty of Science and Engineering
    School
    School of Elec Eng, Comp and Math Sci (EECMS)
    Remarks

    This is an Accepted Manuscript of an article published by Taylor & Francis in Optimization Methods and Software on 23 Apr 2019, available at: https://doi.org/10.1080/10556788.2019.1604704.

    URI
    http://hdl.handle.net/20.500.11937/89486
    Collection
    • Curtin Research Publications
    Abstract

    This paper considers an optimal train control problem with two challenging, non-standard constraints: a speed constraint that is piecewise-constant with respect to the train's position, and control constraints that are non-smooth functions of the train's speed. We formulate this problem as an optimal switching control problem in which the mode switching times are decision variables to be optimized, and the track gradient and speed limit in each mode are constant. Then, using control parameterization and time-scaling techniques, we approximate the switching control problem by a finite-dimensional optimization problem, which is still subject to the challenging speed limit constraint (imposed continuously during each mode) and the non-smooth control constraints. We show that the speed constraint can be transformed into a finite number of point constraints. We also show that the non-smooth control constraints can be approximated by a sequence of conventional (smooth) inequality constraints. The resulting approximate problem can be viewed as a nonlinear programming problem and solved using gradient-based optimization algorithms, where the gradients of the cost and constraint functions are computed via the sensitivity method. A case study using data for a real subway line shows that the proposed method yields a realistic optimal control profile without the undesirable control fluctuations that can occur with the pseudospectral method.

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