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.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Controller Design and Example Analysis Based on RBF Neural Network Tuning


    Contributors:
    Hu, Jie (author) / Zhang, Shengguo (author) / Ma, Jiayu (author) / Zhu, Jiaran (author) / Yu, Changxun (author) / Ma, Chao (author)


    Publication date :

    2023-10-11


    Size :

    3259934 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English




    Tuning of fuzzy logic controller using neural network

    van Cleave, D. / Rattan, K.S. | IEEE | 2000


    Design of QFT-Based Self-Tuning Deadbeat Controller

    H. Mansor / S. B. Mohd Noor | BASE | 2013

    Free access

    Controller Tuning

    Zacher, Serge | Springer Verlag | 2023


    QFT BASED DRAG-FREE AND ATTITUDE CONTROLLER DESIGN/TUNING

    Wu, S.-F. / Fertin, D. / European Space Agency | British Library Conference Proceedings | 2006