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Strip Flatness and Gauge Multivariable Control at Cold Tandem Mill Based on Fuzzy RBF Neural Network

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Intelligence Science and Big Data Engineering (IScIDE 2013)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 8261))

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Abstract

In the process of strip rolling, flatness and gauge control system is a time-delay, coupled and nonlinear complex system. This paper applies fuzzy RBF neural network (FRBF) to cold tandem rolling, and presents a kind of strip flatness and gauge multivariable adaptive control system. The simulation results show that this kind of new controller has good performances of adaptively tracking target and resisting disturbances and is superior to the conventional decoupled PID control in improving the strip flatness and gauge accuracy.

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References

  1. Sun, Y.: Model and Control of Plate Strip Rolling at Cold - Hot Tandem Mill Rolling. Metallurgical Industry Press, Beijing (2010)

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Author information

Authors and Affiliations

  1. Key Laboratory for Advanced Control of Iron and Steel Process (Ministry of Education), School of Automation & Electronic Engineering, University of Science and Technology Beijing, Beijing, 100083, China

    Li Wang

Authors
  1. Li Wang
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Editor information

Editors and Affiliations

  1. School of Automation and Electrical Engineering, University of Science and Technology, Xueyuan Road No. 30, 100083, Beijing, China

    Changyin Sun

  2. Department of Psychology, Peking University, Yiheyuan Road No. 5, 100871, Beijing, China

    Fang Fang

  3. Department of Computer Science and Technology, Nanjing University, Xianlin Avenue No. 163, 210023, Nanjing, China

    Zhi-Hua Zhou

  4. School of Automation, Southeast University, Sipailou No. 2, 210096, Nanjing, China

    Wankou Yang

  5. Institute of Automation, Chinese Academy of Sciences, No. 95 East Zhongguancun Road, 100190, Beijing, China

    Zhi-Yong Liu

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© 2013 Springer-Verlag Berlin Heidelberg

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Wang, L. (2013). Strip Flatness and Gauge Multivariable Control at Cold Tandem Mill Based on Fuzzy RBF Neural Network. In: Sun, C., Fang, F., Zhou, ZH., Yang, W., Liu, ZY. (eds) Intelligence Science and Big Data Engineering. IScIDE 2013. Lecture Notes in Computer Science, vol 8261. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-42057-3_35

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  • DOI: https://doi.org/10.1007/978-3-642-42057-3_35

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-42056-6

  • Online ISBN: 978-3-642-42057-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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