The design of UAV controller has always been a hot topic in related fields, but the traditional controllers have their own limitations: For example, the most classic PID controller has insufficient adjustment ability. For this reason, fuzzy PID controller has been researched, whose anti-interference ability is stronger, but the rule base has many parameters and it is difficult to select. At the same time, reinforcement learning has emerged in some control fields. This paper uses Gymfc as the simulation environment, and takes the fuzzy PID controller as the framework for the mission of UAV to achieve the goal altitude. Through reinforcement learning algorithm A2C, it realizes the self-tuning of the rule base, thus further completing the self-tuning of PID parameters.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Fuzzy PID Controller for UAV Based on Reinforcement Learning


    Weitere Titelangaben:

    Lect. Notes Electrical Eng.


    Beteiligte:
    Fu, Wenxing (Herausgeber:in) / Gu, Mancang (Herausgeber:in) / Niu, Yifeng (Herausgeber:in) / Zhang, Benyi (Autor:in) / Zhang, Weiping (Autor:in) / Mou, Jiawang (Autor:in) / Yang, Runmin (Autor:in) / Zhang, Yichen (Autor:in)

    Kongress:

    International Conference on Autonomous Unmanned Systems ; 2022 ; Xi'an, China September 23, 2022 - September 25, 2022



    Erscheinungsdatum :

    10.03.2023


    Format / Umfang :

    9 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch





    Hybrid Reinforcement Learning based controller for autonomous navigation

    Joglekar, Ajinkya / Krovi, Venkat / Brudnak, Mark et al. | IEEE | 2022


    Improve PID controller through reinforcement learning

    Qin, Yunxiao / Zhang, Weiguo / Shi, Jingping et al. | IEEE | 2018


    Hardware-Compatible Deep Reinforcement Learning-Based Lateral Trajectory Controller

    Abouelnaga, Mohamed / Haberjahn, Mathias / Markert, Daniel et al. | IEEE | 2024


    Interpreting data of reinforcement learning agent controller

    NAGESHRAO SUBRAMANYA / JALES COSTA BRUNO SIELLY / FILEV DIMITAR PETROV | Europäisches Patentamt | 2023

    Freier Zugriff