Journal of Central South University of Technology

, Volume 12, Issue 6, pp 699–704 | Cite as

Modeling and identification of HAGC system of temper rolling mill

  • He Shang-hong Email author
  • Zhong Jue 


Including servo valve, hydraulic cylinder, mill and sensor and ignoring nonlinear factors, the linear dynamic model of hydraulic automatic gage control (HAGC) system of a temper rolling mill was theoretically derived. The order of the model is 4/4, and can be reduced to 2/2. Based on modulating functions method, utilizing numerical integration, we constructed the equivalent identification model of HAGC, and the least square estimation algorithm was established. The input and output data were acquired on line at temper rolling mill in Shangshai Baosteel Group Corporation, and the continuous time model of HAGC system was estimated with the proposed method. At different modulating window intervals, the estimated parameters changed remarkably. When the frequency band-width of modulating filter matches that of estimated system, the parameters can be estimated accurately. Finally, the dynamic model of the HAGC was obtained and validated based on the spectral analysis result.

Key words

hydraulic automatic gage control modeling identification temper rolling mill 

CLC number



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  1. [1]
    Ginzburg V B. Dynamic characteristics of automatic gage control system with hydraulic actuators[J]. Iron and Steel Engineer, 1984, 1: 57–65.Google Scholar
  2. [2]
    Guo R M. Evaluation of dynamic characteristics of HAGC system[J]. Iron and Steel Engineer, 1991, 7: 52–63.Google Scholar
  3. [3]
    GAO Ying-jie, ZHAO Jing-yi, KONG Xiang-dong, et al. Dynamical simulation of hydraulic AGC system in a strip mill[J]. China Mechanical Engineering, 1998, 9(7): 23–26. (in Chinese)Google Scholar
  4. [4]
    Hnbehauen H, Rao G P. A review of identification in continuous-time systems[J]. Annual Review in Control, 1998, 22: 145–171.CrossRefGoogle Scholar
  5. [5]
    Sagara S, Zhao Z Y. Numerical integration approach to on-line identification of continuous time systems[J]. Automatica, 1990, 26(1): 63–74.MathSciNetCrossRefGoogle Scholar
  6. [6]
    Shinbrot M. On the analysis of linear and nonlinear systems[J]. Trans ASME, 1957, 79(3): 547–552.MathSciNetGoogle Scholar
  7. [7]
    Co T B, Ydstie B E. System identification using modulating functions and Fourier transforms[J]. Computers Chem Eng, 1990, 14(10): 1051–1066.CrossRefGoogle Scholar
  8. [8]
    Preisig H A, Rippin D W T. Theory and application of the modulating function method—III: application to industrial process, a well-stirred tank reactor [J]. Computer Chem Engng, 1993, 17(1): 29–39.CrossRefGoogle Scholar
  9. [9]
    Balestrino A, Landi A, Sani L. Parameter identification of continuous systems with multiple-input time delays via modulating Functions[J]. IEE Proc D Contr Theory Appl, 2000, 147(1): 19–27.CrossRefGoogle Scholar
  10. [10]
    HE Shang-hong, ZHONG Jue. Identification of linear continuous-time system using wavelet modulating filters [J]. System Engineering and Electronics, 2004, 15(3): 270–277.Google Scholar
  11. [11]
    HU Guang-shu. Digital Signal Processing—Theory, Algorithms, and Implementation[M]. Beijing: Tsinghua University Press, 1997. (in Chinese)Google Scholar

Copyright information

© Central South University 2005

Authors and Affiliations

  1. 1.School of Automobile and Mechanical EngineeringChangsha University of Science and TechnologyChangshaChina
  2. 2.School of Mechanical and Electrical EngineeringCentral South UniversityChangshaChina

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