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Dynamic Optimization of Feedforward Automatic Gauge Control Based on Extended Kalman Filter

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Abstract

Automatic gauge control is an essentially nonlinear process varying with time delay, and stochastically varying input and process noise always influence the target gauge control accuracy. To improve the control capability of feedforward automatic gauge control, Kalman filter was employed to filter the noise signal transferred from one stand to another. The linearized matrix that the Kalman filter algorithm needed was concluded; thus, the feedforward automatic gauge control architecture was dynamically optimized. The theoretical analyses and simulation show that the proposed algorithm is reasonable and effective.

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Authors and Affiliations

  1. School of Information Engineering, University of Science and Technology Beijing, Beijing, 100083, China

    Bin-hu Yang (Doctor Candidate), Wei-dong Yang, Lian-gui Chen & Lei Qu

Authors
  1. Bin-hu Yang
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  2. Wei-dong Yang
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  3. Lian-gui Chen
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  4. Lei Qu
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Correspondence to Bin-hu Yang.

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Yang, Bh., Yang, Wd., Chen, Lg. et al. Dynamic Optimization of Feedforward Automatic Gauge Control Based on Extended Kalman Filter. J. Iron Steel Res. Int. 15, 39–42 (2008). https://doi.org/10.1016/S1006-706X(08)60028-X

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  • DOI: https://doi.org/10.1016/S1006-706X(08)60028-X

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