Design Tension Controller for the Rewinding System of Roll-to-roll Precision Coating Machine

Wang, Ziyu; Liu, Shanhui; Jiao, Feiqiang; Zhang, Zengqiang; Chen, Weifeng

doi:10.1109/imcec51613.2021.9482045

Cited by 6 publications

(5 citation statements)

References 10 publications

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“…According to the Ref. [15], the angular velocity ω i is converted into linear velocity V i , and the nonlinear coupling model is linearized by using the first-order Taylor formula.…”

Section: Linearization Of Tension System Modelmentioning

confidence: 99%

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Design Decoupling Controller based on Feedforward and Improved PID for Tension System

Ding¹,

Liu²,

Zhang³

et al. 2023

jist

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It is difficult to precisely control the substrate tension due to the tension system of roll-to-roll coating machine with strong coupling and time-varying parameters. Therefore, a feedforward improved proportional integral derivative (PID) parameters self-tuning decoupling controller of tension system is proposed in this paper. According to the components and working principle of the coating machine, the nonlinear coupling mechanism model of the global tension system is established, and the model is linearized by using the Taylor series. Based on the linear model, a feedforward improved PID parameters self-tuning decoupling controller is designed for tension system. Through the feedforward control compensates the modeled tension and speed interference, a tracking differentiator (TD) is used to improve the performance of PID, and the online self-tuning of PID parameters can be realized by an Radial Basis Function (RBF) identification network. The simulation results show that the proposed decoupling control strategy of tension system can well suppress the coupling interference between the units, and realize the real-time tuning of PID parameters.

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“…According to the Ref. [15], the angular velocity ω i is converted into linear velocity V i , and the nonlinear coupling model is linearized by using the first-order Taylor formula.…”

Section: Linearization Of Tension System Modelmentioning

confidence: 99%

“…Combined with the tension linearization model and Ref. [15], the feedforward controller structure of the tension system is shown in Figure 4. V i (s) and T i−1 (s) are the velocity and tension disturbance of each unit, respectively.…”

Section: Design Decoupling Control Strategy 31 Feedforward Controllermentioning

confidence: 99%

Design Decoupling Controller based on Feedforward and Improved PID for Tension System

Ding¹,

Liu²,

Zhang³

et al. 2023

jist

View full text Add to dashboard Cite

show abstract

“…So the nonlinear coupling model of the tension system is linearized by the first-order Taylor formula. According to the reference [23], formula (1) can be rewritten as:…”

Section: Linearization Of the Global Coupling Modelmentioning

confidence: 99%

“…To effectively solve the strong coupling problem of the tension system, it is necessary to design the feedforward controller for velocity and tension perturbation in the tension model. Combining the tension linearization model and the reference [23], the structure of the designed feedforward ADRC controller is shown in Fig. 3.…”

Section: Design Feedforward Adrc Controller For Tension Systemmentioning

confidence: 99%

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

Liu¹,

Ding²,

Wang³

et al. 2023

Journal of Renewable Materials

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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, and the global coupling model is linearized based on the first-order Taylor formula. Secondly, according to the linear model of the tension system, a parameters self-tuning decoupling algorithm of the tension system is proposed by integrating feedforward control, ADRC and RBF. Finally, the simulation results show that the proposed tension control strategy has good decoupling control performance and effectively improves the tension control accuracy for the coating machine.

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“…The materials have characteristics such as thin, continuous, elastic, and easy to be damaged during transportation and handling, so WTS requires not only high technology in terms of mechanics but also high requirements on control engineering. In fact, the controllers used for WTS are mainly proportional-integral (PI) and proportional-integral-derivative (PID) [6]- [8], feedforward control [9], and PID with feedforward control [10]. However, this is a linear control method, while WTS is a strongly nonlinear system, affected by external noise, so it does not always give the desired quality, but the often slow response, low stability, low accuracy, and challenge to meet the increasingly high requirements of today's modern production lines.…”

Section: Introductionmentioning

confidence: 99%

Backstepping control with radial basis function neural network for web transport systems

Ly,

Loc,

Thang

et al. 2023

Bulletin EEI

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Web transport system (WTS) is commonly used in the production and handling of web materials such as paper, fabric, corrugated iron, steel, and printing operations. These materials are easily damaged if the process performance is poor. Therefore, high technology in mechanics and precise control techniques are required in this system. In addition, because of parameter variation, strong nonlinearity, and many external noises, there are many challenges in controlling this process. This paper proposes a backstepping technique-based algorithm to control the web’s tension and velocity. To solve the parameter variation, a radial basis function (RBF) neural network-based adaptation algorithm is developed to approximate the varied components in the control algorithm. The system stability is guaranteed using the Lyapunov stability theorem. Simulations in MATLAB/Simulink have been done and the effectiveness of the proposed control algorithm is verified. Tension and velocity tracking can be obtained with parameter variations.

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Design Tension Controller for the Rewinding System of Roll-to-roll Precision Coating Machine

Cited by 6 publications

References 10 publications

Design Decoupling Controller based on Feedforward and Improved PID for Tension System

Design Decoupling Controller based on Feedforward and Improved PID for Tension System

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

Backstepping control with radial basis function neural network for web transport systems

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