A new model predictive convex programming is proposed in this paper for state and input constrained vehicle guidance design. The proposed method defines a convex optimization framework considering a flexibly designed cost function subject to inequality constraints and a sensitivity relation between state increments and input corrections. This formulated convex optimization problem can be solved in a computationally efficient manner. Simulation studies of nonlinear missile and aircraft landing guidance problems demonstrate the effectiveness of the proposed approach.


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    Titel :

    Model Predictive Convex Programming for Constrained Vehicle Guidance


    Beteiligte:
    Hong, Haichao (Autor:in) / Maity, Arnab (Autor:in) / Holzapfel, Florian (Autor:in) / Tang, Shengjing (Autor:in)


    Erscheinungsdatum :

    2019-10-01


    Format / Umfang :

    4093883 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch