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

Jia Chunying; Chen Yuchen; Ding Zhigang

doi:doi:10.11648/j.acis.20140206.11

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

Jia Chunying, Chen Yuchen, Ding Zhigang

Published in Automation, Control and Intelligent Systems (Volume 2, Issue 6)

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), 2024. Published by Science Publishing Group

Keywords

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

References

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[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

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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

Jia Chunying

College of Electronic and Electric Engineering, Shanghai University of Engineering Science, Shanghai, China
Chen Yuchen

College of Electronic and Electric Engineering, Shanghai University of Engineering Science, Shanghai, China
Ding Zhigang

Shanghai Computer Software Technology Development Center, Shanghai, China

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Plain Text BibTeX RIS

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

Copy | Download

@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}
}

Copy | Download

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  -

Copy | Download