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Journal of Control Science and Engineering
Volume 2017 (2017), Article ID 1952594, 11 pages
Research Article

Parameter Identification and Control Scheme for Monitoring Automatic Thickness Control System with Measurement Delay

1School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, China
2Department of Chemical Engineering, University of Waterloo, Waterloo, ON, Canada

Correspondence should be addressed to Xu Yang

Received 8 March 2017; Revised 24 April 2017; Accepted 10 May 2017; Published 21 June 2017

Academic Editor: Abdul-Qayyum Khan

Copyright © 2017 Xu Yang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


The thickness of the steel strip is an important indicator of the overall strip quality. Deviations in thickness are primarily controlled using the automatic gauge control (AGC) system of each rolling stand. At the last stand, the monitoring AGC system is usually used, where the deviations in thickness can be directly measured by the X-ray thickness gauge device and used as the input to the AGC system. However, due to the physical distance between the thickness detection device and the rolling stand, time delay is unavoidably present in the thickness control loop, which can affect control performance and lead to system oscillations. Furthermore, the parameters of the system can change due to perturbations from external disturbances. Therefore, this paper proposes an identification and control scheme for monitoring AGC system that can handle time delay and parameter uncertainty. The cross-correlation function is used to estimate the time delay of the system, while the system parameters are identified using a recursive least squares method. The time delay and parameter estimates are then further refined using the Levenberg-Marquardt algorithm, so as to provide the most accurate parameter estimates for the complete system. Simulation results show that, compared with the standard Proportion Integration Differentiation (PID) controller approach, the proposed approach is not affected by changes in the time delay and parameter uncertainties.