With the increasing complexity of modern industrial processes, the traditional PID control cannot achieve satisfactory results for the nonlinear systems in industrial control. This article studies the structure and calculation methods of radial basis function neural networks, and on this basis, designs a PID controller based on radial basis function (RBF) neural network tuning. Provide examples of secondorder systems and make changes to the parameters of the second-order system. The PID control algorithm tuned by RBF neural network was simulated using simulation software. Through the simulation of the system's stability, robustness, and anti-interference ability, it was verified that the control system has good stability, robustness, and anti-interference ability.


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    Titel :

    Controller Design and Example Analysis Based on RBF Neural Network Tuning


    Beteiligte:
    Hu, Jie (Autor:in) / Zhang, Shengguo (Autor:in) / Ma, Jiayu (Autor:in) / Zhu, Jiaran (Autor:in) / Yu, Changxun (Autor:in) / Ma, Chao (Autor:in)


    Erscheinungsdatum :

    11.10.2023


    Format / Umfang :

    3259934 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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