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    Implicit integration with adjoint sensitivity propagation for optimal control problems involving differential-algebraic equations

    257692.pdf (185.4Kb)
    Access Status
    Open access
    Authors
    Jiang, C.
    Xie, K.
    Guo, Z.
    Teo, Kok Lay
    Date
    2017
    Type
    Conference Paper
    
    Metadata
    Show full item record
    Citation
    Jiang, C. and Xie, K. and Guo, Z. and Teo, K.L. 2017. Implicit integration with adjoint sensitivity propagation for optimal control problems involving differential-algebraic equations, in Proceedings of the 36th Chinese Control Conference, Jul 26-28 2017, pp. 2489-2494. Dalian, China: IEEE.
    Source Title
    Chinese Control Conference, CCC
    DOI
    10.23919/ChiCC.2017.8027734
    ISBN
    9789881563934
    School
    Department of Mathematics and Statistics
    Remarks

    © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

    URI
    http://hdl.handle.net/20.500.11937/59275
    Collection
    • Curtin Research Publications
    Abstract

    For the solution of optimal control problem involving an index-1 differential-algebraic equation, an efficient function evaluation algorithm is proposed in this paper. In the evaluation procedure, the state equation is propagated forwards, then, adjoint sensitivity is propagated backwards. Thus, it is computationally more efficient than forward sensitivity propagation when the number of constraints is less than that of optimization variables. In order to reduce Newton iterations, the adjoint sensitivity is derived utilizing the implicit function theorem, and the integration procedure is accelerated by incorporating a predictor-corrector strategy. This algorithm is integrated with a nonlinear programming solver Ipopt to solve sequentially the point-to-point optimal control for a Delta robot with constrained motor torque. Numerical experiments demonstrate the efficiency of this algorithm.

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