A model-based predictive control scheme for steal rolling mills using neural networks

Abstract

A capital issue in roll-gap control for rolling mill plants is the difficulty to measure the output thickness without including time delays in the control loop. Time delays are a consequence of the possible locations for the output thickness sensor, which usually is located some distance away from the roll gap. In this work, a new model-based predictive control law is proposed. The new scheme is a neural network based predictive control structure which is applied to roll-gap control with outstanding results. It is shown that the neural network based predictive control permits to overcome the existing time delays in the system dynamics. The proposed scheme implements a virtual thickness sensor, which releases an accurate estimate of the actual output thickness. It is shown that the dynamic response of the rolling mill system can be substantially improved by using the proposed controller. Simulation results are presented to illustrate the controller performance

Flatness Control Strategy Based on Delay Compensation for Cold Rolling Mill

“…Because of the complexity of pure delay control, studies on the control method and mechanism of industrial control process with time delay have received more and more attention. For the time delay compensation control in cold rolling process, a lot of novel control strategies have been proposed based on traditional or artificial intelligent methods . For example, Wang et al proposed a new Smith predictor to improve the stability and response performance of the flatness control system, and a single neural network was employed into the Smith predictor to improve the controller's self‐adaptability .…”

Neural network-based model predictive control of a servo-hydraulic vehicle suspension system

“…APC can be readily tuned by intuitive means, it is good for systems with time delay and it is flexible, thus effective in a wide spectrum of control application [24]. 23 -25 September 2009, Nairobi, Kenya NNMPC has so many metallurgical process, chemical plants, food and pharmaceutical procesing applications as made evident in the quantity of available literature [25] , the converse is the case for ASS problems. The prevailing need for real-time control of ASS with adequate handling of design constraints is a motivation for this study.…”

Representation and control of the cold rolling process through artificial neural networks via sensitivity factors

“…Neural networks (NN) have been the focus of great attention due to its capacity in solving non-linear problems for learning (Hunt et al, 1992;Tai et al, 1992;Yamada and Yabuta, 1993;Sbarbaro-Hofer et al, 1993;Guez et al, 1988). The application of ANN in several metallurgical processes can be seen in extensive literature (Son et al, 2004;Andersen et al, 1992;Smartt, 1992;Zárate et al, 1998a;Zárate, 1998;Gunasekera et al, 1998;Zárate and Bittencout, 2001;Shlang et al, 2001;Zárate and Bittencout, 2002;Kim et al, 2002;Gálvez et al, 2003;Yang et al, 2004b;Son et al, 2005). For example, in Ref.…”