In this paper, a novel event-triggered optimizing consensus tracking control scheme is proposed for multiple unmanned aerial vehicles (UAVs) formation. First, the formation consensus tracking is transformed to an optimal control issue based on the feedforward-feedback control design framework. Subsequently, a policy iteration algorithm is developed to solve the input-constrained Hamilton-Jacobi-Isaacs (HJI) equation based on the two-player zero-sum differential game. The critic-only architecture is adopted to approximate the optimal cost function. A novel weight vector tuning law is designed to relax the common initial admissible control (IAC) condition and persistence of excitation (PE) condition by supplementing appropriate compensation terms. Moreover, a novel input-oriented relative threshold dynamic event-triggered mechanism is proposed to alleviate the control transmission burdens. Finally, the simulation results demonstrate the effectiveness of proposed consensus tracking control strategy.


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    Titel :

    Optimizing Consensus Tracking Control of Multiple UAVs Formation via Event-Triggered Critic Learning Neurodynamic Programming


    Beteiligte:
    Zhou, Chuhan (Autor:in) / Wang, Ying (Autor:in) / Lv, Maolong (Autor:in) / Park, Ju H. (Autor:in)

    Erschienen in:

    Erscheinungsdatum :

    01.11.2024


    Format / Umfang :

    4347946 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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