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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    Title :

    Model Predictive Convex Programming for Constrained Vehicle Guidance


    Contributors:


    Publication date :

    2019-10-01


    Size :

    4093883 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English