In this paper, an adaptive neural network (NN) composite anti-disturbance control scheme is proposed for the unmanned autonomous helicopter (UAH) with unknown disturbance and system uncertainty. Considering the characteristic of vertical take-off and landing, the altitude and attitude nonlinear model of the UAH is established. The radial basis function NNs are adopted to approximate the system uncertainty. Meanwhile, an extended state observer (ESO) is constructed to cope with the unknown disturbance based on the outputs of the NNs. Combining with the backstepping technique, a composite anti-disturbance tracking control strategy is developed to obtain satisfactory system performance. Finally, the simulation results show the availability of the presented robust controller.


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    Title :

    Composite Anti-disturbance Tracking Control for Unmanned Autonomous Helicopter Using Neural Network and Extended State Observer


    Additional title:

    Lect. Notes Electrical Eng.


    Contributors:
    Ren, Zhang (editor) / Wang, Mengyi (editor) / Hua, Yongzhao (editor) / Yan, Kun (author) / Chen, Chaobo (author) / Gao, Song (author) / Cao, Kai (author) / Zhou, Jie (author)


    Publication date :

    2022-07-29


    Size :

    11 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




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