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Application in Composite Machine Using RBF Neural Network Based on PID Control

Received: 10 November 2014     Accepted: 24 November 2014     Published: 28 November 2014
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Abstract

In the absence of solvent composite machine, because the radius of drum winding and rewinding roller in the transmission process is changing. With the coiled material rolls diameter more and more large, and put the curly size getting smaller and smaller, this has the certain difficulty for the tension control. Therefore, good tension control is non solvent composite is very important. Analyzed the reason and the tension control mathematical model generation composite machine tension in the BOPP production line, for the constant tension control of composite machine, put forward a kind of improved PID control method based on RBF neural network. By the method of Jacobian information identification of RBF neural network, combined with the incremental PID algorithm to realize the self-tuning tension control parameters, control simulation and implementation of the model using Matlab software programming. The simulation results show that, the improved algorithm has better control effect than the general PID.

Published in Automation, Control and Intelligent Systems (Volume 2, Issue 6)
DOI 10.11648/j.acis.20140206.11
Page(s) 100-104
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2014. Published by Science Publishing Group

Keywords

Control PID Algorithm, Jacobian Information Identification, RBF Neural Network, Matlab

References
[1] Iman Poultangari, Reza Shahnazi ,Mansour Sheikhan RBF neural network based PI pitch controller for a class of 5-MW wind turbines using particle swarm optimization algorithm. ISA Transactions 51 (2012) 641–648.
[2] Ismail Yabanova, Ali Keçebas, Development of ANN model for geothermal district heating system and a novel PID-based control strategy. Applied Thermal Engineering 51 (2013) 908-916.
[3] LIU Hong-mei,WANG Shao-ping,OUYANG Ping-chao, Fault Diagnosis in a Hydraulic Position Servo System Using RBF Neural Network. 2006, 19(4), 346-353.
[4] Shi Zhongzhao. Neural network control theory [M]. Xi'an: Northwestern Polytechnical University press, 1999:85-90.
[5] Wang Jiangjiang,Zhang Chunfa,Jing Youyin.Self-adaptive RBF neural network PID control in exhaust temperature of micro-turbine[C] Proceedings of the Seventh International Conference on Machine Learning and Cybernetics,2008:2131-2136.
[6] Elanayar V T S, Shin Y C. Radial basis function neural network for approximation and estimation of nonlinear stochastic dynamic systems [J].IEEE Transactions on Neural Network,1994,5 (4):594-603.
[7] Kee-Hyun Shin, Jeung-In Jang, et al. Compensation Method for Tension Disturbance Due to an Unknown Roll Shape in a Web Transport System [C].IEEE Transactions on industry applications. 2003, 5(39): 1422-1428.
[8] Wei Xinming. Take the tension control system for machine based on BP neural network volume [D]. Northeastern University. 2014
[9] Deng Xiao, Hu Muyi, PID neural network application in the roll paper roll tension control system. China paper journal [J]. 2014.29(1).44-48
[10] Chen Zuojie, Wu Peide, Zhang Yihong. Application of fuzzy PI controller in the study of film tension control system [J]. 2014.3(1).42-44
Cite This Article
  • APA Style

    Jia Chunying, Chen Yuchen, Ding Zhigang. (2014). Application in Composite Machine Using RBF Neural Network Based on PID Control. Automation, Control and Intelligent Systems, 2(6), 100-104. https://doi.org/10.11648/j.acis.20140206.11

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    ACS Style

    Jia Chunying; Chen Yuchen; Ding Zhigang. Application in Composite Machine Using RBF Neural Network Based on PID Control. Autom. Control Intell. Syst. 2014, 2(6), 100-104. doi: 10.11648/j.acis.20140206.11

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    AMA Style

    Jia Chunying, Chen Yuchen, Ding Zhigang. Application in Composite Machine Using RBF Neural Network Based on PID Control. Autom Control Intell Syst. 2014;2(6):100-104. doi: 10.11648/j.acis.20140206.11

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  • @article{10.11648/j.acis.20140206.11,
      author = {Jia Chunying and Chen Yuchen and Ding Zhigang},
      title = {Application in Composite Machine Using RBF Neural Network Based on PID Control},
      journal = {Automation, Control and Intelligent Systems},
      volume = {2},
      number = {6},
      pages = {100-104},
      doi = {10.11648/j.acis.20140206.11},
      url = {https://doi.org/10.11648/j.acis.20140206.11},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.acis.20140206.11},
      abstract = {In the absence of solvent composite machine, because the radius of drum winding and rewinding roller in the transmission process is changing. With the coiled material rolls diameter more and more large, and put the curly size getting smaller and smaller, this has the certain difficulty for the tension control. Therefore, good tension control is non solvent composite is very important. Analyzed the reason and the tension control mathematical model generation composite machine tension in the BOPP production line, for the constant tension control of composite machine, put forward a kind of improved PID control method based on RBF neural network. By the method of Jacobian information identification of RBF neural network, combined with the incremental PID algorithm to realize the self-tuning tension control parameters, control simulation and implementation of the model using Matlab software programming. The simulation results show that, the improved algorithm has better control effect than the general PID.},
     year = {2014}
    }
    

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  • TY  - JOUR
    T1  - Application in Composite Machine Using RBF Neural Network Based on PID Control
    AU  - Jia Chunying
    AU  - Chen Yuchen
    AU  - Ding Zhigang
    Y1  - 2014/11/28
    PY  - 2014
    N1  - https://doi.org/10.11648/j.acis.20140206.11
    DO  - 10.11648/j.acis.20140206.11
    T2  - Automation, Control and Intelligent Systems
    JF  - Automation, Control and Intelligent Systems
    JO  - Automation, Control and Intelligent Systems
    SP  - 100
    EP  - 104
    PB  - Science Publishing Group
    SN  - 2328-5591
    UR  - https://doi.org/10.11648/j.acis.20140206.11
    AB  - In the absence of solvent composite machine, because the radius of drum winding and rewinding roller in the transmission process is changing. With the coiled material rolls diameter more and more large, and put the curly size getting smaller and smaller, this has the certain difficulty for the tension control. Therefore, good tension control is non solvent composite is very important. Analyzed the reason and the tension control mathematical model generation composite machine tension in the BOPP production line, for the constant tension control of composite machine, put forward a kind of improved PID control method based on RBF neural network. By the method of Jacobian information identification of RBF neural network, combined with the incremental PID algorithm to realize the self-tuning tension control parameters, control simulation and implementation of the model using Matlab software programming. The simulation results show that, the improved algorithm has better control effect than the general PID.
    VL  - 2
    IS  - 6
    ER  - 

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Author Information
  • College of Electronic and Electric Engineering, Shanghai University of Engineering Science, Shanghai, China

  • College of Electronic and Electric Engineering, Shanghai University of Engineering Science, Shanghai, China

  • Shanghai Computer Software Technology Development Center, Shanghai, China

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