
FOLLOWUS
Institute of Cyber-Systems and Control, Zhejiang University, Hangzhou 310027, China
Zhejiang SUPCON Research Co., Ltd., Hangzhou 310053, China
Zai-sheng PAN, panzs@zju.edu.cn
收稿:2016-07-06,
修回:2018-;7-;8,
纸质出版:2018-07
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潘再生, 周玄昊, 陈鹏. 基于神经网络的热镀锌层厚控制系统的研究与应用[J]. 信息与电子工程前沿(英文), 2018,19(7):834-846.
Zai-sheng PAN, Xuan-hao ZHOU, Peng CHEN. Development and application of a neural network based coating weight control system for a hot-dip galvanizing line[J]. Frontiers of Information Technology & Electronic Engineering, 2018, 19(7): 834-846.
潘再生, 周玄昊, 陈鹏. 基于神经网络的热镀锌层厚控制系统的研究与应用[J]. 信息与电子工程前沿(英文), 2018,19(7):834-846. DOI: 10.1631/FITEE.1601397.
Zai-sheng PAN, Xuan-hao ZHOU, Peng CHEN. Development and application of a neural network based coating weight control system for a hot-dip galvanizing line[J]. Frontiers of Information Technology & Electronic Engineering, 2018, 19(7): 834-846. DOI: 10.1631/FITEE.1601397.
钢铁生产企业中热镀锌生产线是一个典型的订单驱动离散制造过程。该系统呈现许多复杂动态特性,包括大时变系统时滞、强非线性和不可测的扰动项。这些因素增大了在线镀层厚度控制系统的设计难度。提出一种新的基于神经网络的控制方法,并成功应用在华菱涟钢集团的热镀锌生产线。实际生产运行结果表明,镀层厚度的波动性以及产品规格切换时的过渡时间显著减小,验证了该方法的有效性。
The hot-dip galvanizing line (HDGL) is a typical order-driven discrete-event process in steelmaking. It has some complicated dynamic characteristics such as a large time-varying delay
strong nonlinearity
and unmeasured disturbance
all of which lead to the difficulty of an online coating weight controller design. We propose a novel neural network based control system to solve these problems. The proposed method has been successfully applied to a real production line at VaLin LY Steel Co.
Loudi
China. The industrial application results show the effectiveness and efficiency of the proposed method
including significant reductions in the variance of the coating weight and the transition time.
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