Eccentricity and hardness control in cold rolling mills with a dynamically constructed neural controller

Frayman, Y. and Rolfe, B.F. 2003, Eccentricity and hardness control in cold rolling mills with a dynamically constructed neural controller, in IPMM 2003 : Intelligence in a small materials world : selected papers from the Fourth International Intelligent Processing and Manufacturing of Materials Conference, Intelligent Processing and Manufacturing of Materials, Sendai, Japan, pp. 199-214.


Title Eccentricity and hardness control in cold rolling mills with a dynamically constructed neural controller
Author(s) Frayman, Y.
Rolfe, B.F.
Conference name International Conference on Intelligent Processing and Manufacturing of Materials (4th : 2003 : Sendai, Japan)
Conference location Sendai, Japan
Conference dates 18-23 May 2003
Title of proceedings IPMM 2003 : Intelligence in a small materials world : selected papers from the Fourth International Intelligent Processing and Manufacturing of Materials Conference
Editor(s) Meech, John A.
Kawazoe, Yoshiyuki
Kumar, Vijay
Maguire, John F.
Publication date 2003
Start page 199
End page 214
Publisher Intelligent Processing and Manufacturing of Materials
Place of publication Sendai, Japan
Summary The main objective of a steel strip rolling process is to produce high quality steel at a desired thickness.  Thickness reduction is the result of the speed difference between the incoming and the outgoing steel strip and the application of the large normal forces via the backup and the work rolls.  Gauge control of a cold rolled steel strip is achieved using the gaugemeter principle that works adequately for the input gauge changes and the strip hardness changes.  However, the compensation of some factors is problematic, for example, eccentricity of the backup rolls.  This cyclic eccentricity effect causes a gauge deviation, but more importantly, a signal is passed to the gap position control so to increase the eccentricity deviation.  Consequently, the required high product tolerances are severely limited by the presence of the roll eccentricity effects.
In this paper a direct model reference adaptive control (MRAC) scheme with dynamically constructed neural controller was used.  The aim here is to find the simplest controller structure capable of achieving an optimal performance.  The stability of the adaptive neural control scheme (i.e. the requirement of persistency of excitation and bounded learning rates) is addressed by using as the inputs to the reference model the plant's state variables.  In such a case, excitation is due to actual plant signals (states) affected by plant disturbances and noise.  In addition, a reference model in the form of a filter with a desired transfer function using Modulus Optimum design was used to ensure variance in the desired dynamic characteristics of the system.  The gradually decreasing learning rate employed by the neural controller in this paper is aimed at eliminating controller instability resulting from over-aggressive control.  The moving target problem (i.e. the difficulty of global neural networks to perfrom several separate computational tasks in closed -loop control) is addressed by the localized architecture of the controller.  The above control scheme and learning algorithm offers a method for automatic discovery of an efficient controller.
The resulting neural controller produces an excellent disturbance rejection in both cases of eccentricity and hardness disturbances, reducing the gauge deviation due to eccentricity disturbance from 33.36% to 4.57% on average, and the gauge deviation due to hardness disturbance from 12.59% to 2.08%.
ISBN 1932078193
9781932078190
Language eng
Field of Research 091302 Automation and Control Engineering
HERDC Research category E2 Full written paper - non-refereed / Abstract reviewed
Persistent URL http://hdl.handle.net/10536/DRO/DU:30014030

Document type: Conference Paper
Collection: School of Engineering and Technology
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