This study advances flight control systems by integrating deep reinforcement learning to enhance fault tolerance in fixed-wing aircraft. We assess the efficiency of Cross-Entropy Method Reinforcement Learning (CEM-RL) and Proximal Policy Optimization (PPO) algorithms in developing an adaptive stable attitude controller. Our proposed frameworks, focusing on smooth actuator control, showcase improved robustness across standard and fault-induced scenarios. The algorithms demonstrate unique traits in terms of trade-offs between trajectory tracking and control smoothness. Our approach that results in state-of-the-art performance with respect to benchmarks, presents a leap forward in autonomous aviation safety.


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    Titel :

    Towards Intelligent Fault-Tolerant Attitude Control of Fixed-Wing Aircraft


    Weitere Titelangaben:

    Lect. Notes Electrical Eng.


    Beteiligte:
    Yan, Liang (Herausgeber:in) / Duan, Haibin (Herausgeber:in) / Deng, Yimin (Herausgeber:in) / Zongo, Alex B. (Autor:in) / Qing, Li (Autor:in)

    Kongress:

    International Conference on Guidance, Navigation and Control ; 2024 ; Changsha, China August 09, 2024 - August 11, 2024



    Erscheinungsdatum :

    05.03.2025


    Format / Umfang :

    10 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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