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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    Title :

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


    Additional title:

    Lect. Notes Electrical Eng.


    Contributors:
    Yan, Liang (editor) / Duan, Haibin (editor) / Deng, Yimin (editor) / Zongo, Alex B. (author) / Qing, Li (author)

    Conference:

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



    Publication date :

    2025-03-05


    Size :

    10 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




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