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    A sequential computational approach to optimal control problems for differential-algebraic systems based on efficient implicit Runge–Kutta integration

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    Authors
    Jiang, C.
    Xie, K.
    Yu, C.
    Yu, M.
    Wang, H.
    He, Y.
    Teo, Kok Lay
    Date
    2018
    Type
    Journal Article
    
    Metadata
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    Citation
    Jiang, C. and Xie, K. and Yu, C. and Yu, M. and Wang, H. and He, Y. and Teo, K.L. 2018. A sequential computational approach to optimal control problems for differential-algebraic systems based on efficient implicit Runge–Kutta integration. Applied Mathematical Modelling. 58: pp. 313-330.
    Source Title
    Applied Mathematical Modelling
    DOI
    10.1016/j.apm.2017.05.015
    ISSN
    0307-904X
    School
    School of Electrical Engineering, Computing and Mathematical Science (EECMS)
    URI
    http://hdl.handle.net/20.500.11937/67710
    Collection
    • Curtin Research Publications
    Abstract

    Efficient and reliable integrators are indispensable for the design of sequential solvers for optimal control problems involving continuous dynamics, especially for real-time applications. In this paper, optimal control problems for systems represented by index-1 differential-algebraic equations are investigated. On the basis of a time-scaling transformation, the control is parameterized as a piecewise constant function with variable heights and switching time instants. Compared with control parameterization with fixed time grids, the flexibility of adjusting switching time instants increases the chance of finding the optimal solution. Furthermore, error constraints are introduced in the optimization procedure such that the optimal control obtained has a guarantee of integration accuracy. For the derived approximate nonlinear programming problem, a function evaluation and forward sensitivity propagation algorithm is proposed with an embedded implicit Runge–Kutta integrator, which executes one Newton iteration in the limit by employing a predictor-corrector strategy. This algorithm is combined with a nonlinear programming solver Ipopt to construct the optimal control solver. Numerical experiments for the solution of the optimal control problem for a Delta robot demonstrate that the computational speed of this solver is increased by a factor of 0.5–2 when compared with the same solver without the predictor-corrector strategy, and increased by a factor of 20–40 when compared with solver embedding IDAS, the Implicit Differential-Algebraic solver with Sensitivity capabilities developed by Lawrence Livermore National Laboratory. Meanwhile, the accuracy loss compared with the one using IDAS is small and admissible.

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