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Research on the Application Mechanism of Single Neuron SAC Algorithm in Feedforward Compensation System Based on Invariance Principle about Hot Strip Mill

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Advances in Neural Networks – ISNN 2012 (ISNN 2012)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 7368))

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Abstract

For the coupling characteristics about strip shape and gauge integrated system in hot strip mill, the feedforward compensation control method based on invariance principle was proposed, so the coupling problem of control system was effectively solved. The astringency mechanism and the stability mechanism of single neuron simple adaptive control were thorough analysis in this paper, and its application in strip shape and gauge integrated system of hot strip mill was realized. The simulation experiments achieve desirable control and thus prove its validity and feasibility.

This paper is supported by the project from Beijing Key Discipline Development Program (No. XK100080537) and the Fundamental Research Funds for the Central Universities.

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References

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Author information

Authors and Affiliations

  1. School of Automation & Electrical Engineering, University of Science and Technology, Beijing, China

    Baoyong Zhao & Yixin Yin

Authors
  1. Baoyong Zhao
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  2. Yixin Yin
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Editor information

Editors and Affiliations

  1. Department of Mechanical & Automation Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong

    Jun Wang

  2. School of Electrical and Computer Engineering, Oklahoma State University, 74078, Stillwater, OK, USA

    Gary G. Yen

  3. Department of Electrical and Computer Engineering, University of Cyprus, 75 Kallipoleos Avenue, 1678, Nicosia, Cyprus

    Marios M. Polycarpou

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© 2012 Springer-Verlag Berlin Heidelberg

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Zhao, B., Yin, Y. (2012). Research on the Application Mechanism of Single Neuron SAC Algorithm in Feedforward Compensation System Based on Invariance Principle about Hot Strip Mill. In: Wang, J., Yen, G.G., Polycarpou, M.M. (eds) Advances in Neural Networks – ISNN 2012. ISNN 2012. Lecture Notes in Computer Science, vol 7368. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31362-2_48

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  • DOI: https://doi.org/10.1007/978-3-642-31362-2_48

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-31361-5

  • Online ISBN: 978-3-642-31362-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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