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  • Open Access


    Research on Anti-Fluctuation Control of Winding Tension System Based on Feedforward Compensation

    Yujie Duan1, Jianguo Liang1,*, Jianglin Liu1, Haifeng Gao1, Yinhui Li2, Jinzhu Zhang1, Xinyu Wen3

    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.2, pp. 1239-1261, 2024, DOI:10.32604/cmes.2023.044400

    Abstract In the fiber winding process, strong disturbance, uncertainty, strong coupling, and fiber friction complicate the winding constant tension control. In order to effectively reduce the influence of these problems on the tension output, this paper proposed a tension fluctuation rejection strategy based on feedforward compensation. In addition to the bias harmonic curve of the unknown state, the tension fluctuation also contains the influence of bounded noise. A tension fluctuation observer (TFO) is designed to cancel the uncertain periodic signal, in which the frequency generator is used to estimate the critical parameter information. Then, the fluctuation More >

  • Open Access


    An ADRC Parameters Self-Tuning Control Strategy of Tension System Based on RBF Neural Network

    Shanhui Liu1,*, Haodi Ding1, Ziyu Wang1, Li’e Ma1, Zheng Li2

    Journal of Renewable Materials, Vol.11, No.4, pp. 1991-2014, 2023, DOI:10.32604/jrm.2022.023659

    Abstract High precision control of substrate tension is the premise and guarantee for producing high-quality products in roll-to-roll precision coating machine. However, the complex relationships in tension system make the problems of decoupling control difficult to be solved, which has limited the improvement of tension control accuracy for the coating machine. Therefore, an ADRC parameters self-tuning decoupling strategy based on RBF neural network is proposed to improve the control accuracy of tension system in this paper. Firstly, a global coupling nonlinear model of the tension system is established according to the composition of the coating machine, More > Graphic Abstract

    An ADRC Parameters Self-Tuning Control Strategy of Tension System Based on RBF Neural Network

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