This paper presents an improved model reference adaptive controller (I-MRAC) with RBF neural network approximation to deal with the model uncertainties, unknown actuator dynamics and input saturation of the unmanned aerial vehicle (UAV). On the one hand, the output of the RBF neural network (NN) is used as the compensator to eliminate the uncertainties of the system. On the other hand, the reference model is modified to deal with the unknown actuator dynamics and input saturation, improve stability and robustness, and prevent the high frequency oscillations. Meanwhile, the stability of the whole closed-loop system is proved by the Lyapunov analysis. The numerical simulation results of UAV attitude control demonstrate the effectiveness of the proposed method.


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    Title :

    Improved Model Reference Adaptive Controller with RBF Neural Network Approximation for UAV


    Additional title:

    Lect. Notes Electrical Eng.


    Contributors:
    Wu, Meiping (editor) / Niu, Yifeng (editor) / Gu, Mancang (editor) / Cheng, Jin (editor) / Gai, Wendong (author) / Liu, Yang (author) / Zhang, Jing (author) / Zhang, Guilin (author)

    Conference:

    International Conference on Autonomous Unmanned Systems ; 2021 ; Changsha, China September 24, 2021 - September 26, 2021



    Publication date :

    2022-03-18


    Size :

    9 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




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