Unmanned Aerial Vehicles (UAVs) have been extensively used in civil and industrial applications due to the rapid development of the involved technologies. Especially, using deep reinforcement learning methods for motion control acquires a major progress recently since deep Q-learning has successfully applied to the continuous action domain problem. This paper proposes a new Deep Deterministic Policy Gradient (DDPG) algorithm for path following control problem of UAV with sensor faults. Firstly, the model of UAV path following problem has been established. After that, the DDPG framework is constructed. Then, the proposed DDPG algorithm is formulated to the path following problem. Finally, simulation results are carried out to show the efficiency and effectiveness of the proposed methodology.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    A Deep Reinforcement Learning Strategy for UAV Path Following Control Under Sensor Fault


    Weitere Titelangaben:

    Lect. Notes Electrical Eng.


    Beteiligte:
    Yan, Liang (Herausgeber:in) / Duan, Haibin (Herausgeber:in) / Yu, Xiang (Herausgeber:in) / Zhang, Yintao (Autor:in) / Zhang, Youmin (Autor:in) / Yu, Ziquan (Autor:in)


    Erscheinungsdatum :

    30.10.2021


    Format / Umfang :

    11 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch





    A Deep Reinforcement Learning Strategy for UAV Path Following Control Under Sensor Fault

    Zhang, Yintao / Zhang, Youmin / Yu, Ziquan | British Library Conference Proceedings | 2022


    Deep Reinforcement Learning for Concentric Tube Robot Path Following

    Iyengar, Keshav / Spurgeon, Sarah / Stoyanov, Danail | BASE | 2023

    Freier Zugriff

    Autonomous Vehicle Driving Path Control with Deep Reinforcement Learning

    Tiong, Teckchai / Saad, Ismail / Teo, Kenneth Tze Kin et al. | IEEE | 2023