This paper is concerned with devising nonlinear optimal guidance for intercepting a stationary target with a desired impact time. According to Pontryagin’s maximum principle, some optimality conditions for the solutions of the nonlinear optimal interception problem are established; and the structure of the corresponding optimal control is presented. By employing the optimality conditions, we formulate a parameterized system so that its solution space is the same as that of the nonlinear optimal interception problem. As a consequence, a simple propagation of the parameterized system, without using any optimization method, is sufficient to generate enough sampled data for the mapping from the current state and time-to-go to the optimal guidance command. By virtue of the universal approximation theorem, a feedforward neural network, trained by the generated data, is able to represent the mapping from the current state and time-to-go to the optimal guidance command. Therefore, the trained network eventually can generate impact-time-constrained nonlinear optimal guidance within a constant time. Finally, the developed nonlinear optimal guidance is exemplified and studied through simulations, showing that the nonlinear optimal guidance law performs better than existing interception guidance laws.


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    Titel :

    Nonlinear Optimal Guidance for Intercepting Stationary Targets with Impact-Time Constraints


    Beteiligte:
    Wang, Kun (Autor:in) / Chen, Zheng (Autor:in) / Wang, Han (Autor:in) / Li, Jun (Autor:in) / Shao, Xueming (Autor:in)

    Erschienen in:

    Erscheinungsdatum :

    2022-05-27


    Format / Umfang :

    13 pages




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch






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