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Research Papers

Online Parameter Estimation for Adaptive Feedforward Control of the Strip Thickness in a Hot Strip Rolling Mill

[+] Author and Article Information
Katharina Prinz

Christian Doppler Laboratory for Model-Based Process Control in the Steel Industry,
Automation and Control Institute (ACIN),
TU Wien, Vienna, Austria
e-mail: prinz@acin.tuwien.ac.at

Andreas Steinboeck

Automation and Control Institute (ACIN),
TU Wien, Vienna, Austria
e-mail: steinboeck@acin.tuwien.ac.at

Martin Müller

Christian Doppler Laboratory for Model-Based Process Control in the Steel Industry,
Automation and Control Institute (ACIN),
TU Wien, Vienna, Austria
e-mail: mueller@acin.tuwien.ac.at

Andreas Ettl

Christian Doppler Laboratory for Model-Based Process Control in the Steel Industry,
Automation and Control Institute (ACIN),
TU Wien, Vienna, Austria
e-mail: ettl@acin.tuwien.ac.at

Florian Schausberger

voestalpine Stahl GmbH,
Linz, Austria
e-mail: florian.schausberger@voestalpine.com

Andreas Kugi

Christian Doppler Laboratory for Model-Based Process Control in the Steel Industry,
Automation and Control Institute (ACIN),
TU Wien, Vienna, Austria
e-mail: kugi@acin.tuwien.ac.at

1Corresponding author.

Manuscript received November 21, 2017; final manuscript received April 14, 2019; published online May 14, 2019. Assoc. Editor: Dragan Djurdjanovic.

J. Manuf. Sci. Eng 141(7), 071005 (May 14, 2019) (12 pages) Paper No: MANU-17-1726; doi: 10.1115/1.4043575 History: Received November 21, 2017; Accepted April 15, 2019

A new adaptive disturbance feedforward control strategy of the strip thickness in a hot strip rolling mill with online parameter estimation is proposed. The feedforward control strategy makes use of the measured strip temperature and strip entry thickness. To avoid that these disturbances cause a nonuniform strip exit thickness, the Sims’ roll gap model and a linear mill stand deflection model are used to compute control inputs, which compensate for these disturbances. By minimizing the difference between the expected roll force from the model and the measured roll force, uncertain parameters of the model and also errors of the strip tracking are estimated in real time. The estimated parameters are immediately used in the adaptive feedforward controller. Experimental results of the proposed control approach obtained from an industrial hot strip rolling mill show a significant improvement of the strip thickness accuracy compared to the existing standard controllers. The proposed adaptive feedforward control strategy is now in permanent operation at the considered rolling mill.

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References

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Figures

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Fig. 1

Side view of the mill stand and applied forces with the deflection of the mill stand FR/m and the position xh of the hydraulic cylinder

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Fig. 2

Structure of the simulation model

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Fig. 3

Plastic deformation of the strip in the roll gap

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Fig. 4

Material tracking and synchronization at the pyrometer measurement

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Fig. 5

Mapping of the pyrometer measurement

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Fig. 6

Closed-loop control structure with the adaptive feedforward control strategy

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Fig. 7

Simulation results with a model-plant mismatch (10% error in m1 of Eq. (1) and 1% of L in Z) (Color version online.)

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Fig. 8

Predicted roll force and identified parameters with a model-plant mismatch (10% error in m1 of Eq. (1) and 1% of L in Z) (Color version online.)

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Fig. 9

Simulation results of the roll force FR for a single estimation horizon with a model-plant mismatch (10% error in m1 of Eq. (1) and 1% of L in Z) (Color version online.)

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Fig. 10

Behavior of the nominal feedforward controller (combined with AGC) at the industrial plant with incorrect nominal sensitivities in the roll force model (24) (Color version online.)

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Fig. 11

Estimation of the roll force for a single estimation horizon (length Zhor = 0.25L) with k^T=0.716 and k^Z=−0.0022L for the strip shown in Fig. 10 (Color version online.)

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Fig. 12

Parameter estimation at the first mill stand for the strip of Fig. 10

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Fig. 13

Estimated parameters and results of the adaptive feedforward controller for a sample strip at the first mill stand

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Fig. 14

Frequency distribution of the standard deviation (28) of the slope-corrected exit thickness at the first mill stand of an industrial finishing mill with linearized nominal feedforward thickness controller and AGC (blue bars, 492 strips), with AGC alone (green bars, 475 strips), and with adaptive feedforward thickness controller and AGC (red bars, 314 strips) (Color version online.)

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