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小波神经网络预测控制在加热炉炉温控制中的应用

Application of wavelet neural network predictive control in the reheating furnace temperature control
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摘要 针对蓄热步进式加热炉大滞后、大惯性等特点,采用小波神经网络建立加热炉炉温预测模型,预测炉温未来输出值,并根据二次型性能指标构建炉温优化控制器,通过滚动优化控制器修正神经网络的参数,得到系统未来的控制量。仿真结果表明,该算法对炉温的变化具有良好的跟踪性,调整周期较短,为其在实际生产中的应用奠定了基础。 Aiming at the large time-delay and large inertia of the regenerative walking-beam furnace,this paper employed the wavelet neural network to establish a prediction model to predict the future of furnace temperature of reheating furnace output value. Moreover,a furnace temperature optimal controller is established according to the quadratic cost function to conduct moving optimization,so that the future control sequence can be obtained. The simulation results indicate that the prediction control of wavelet neural network has good tracking ability and short adjustment period,which lays a solid foundation for its application in practical production.
作者 薛美盛 闵天 高述超 方醒 程祥 秦宇海 XUE Mei-sheng1, MIN Tian1, GAO Shu-chao1, FANG Xing1, CHENG Xiang1, QIN Yu-hai2 ( 1. Dept. of Automation, University of Science and Technology of China, Hefei 230026, China; 2. Jiangsu Panvieo Energy Saving Technology Co., Ltd., Nanjing 211106, China)
出处 《冶金自动化》 2018年第5期19-22,32共5页 Metallurgical Industry Automation
关键词 蓄热式加热炉 小波神经网络 预测控制 炉温控制 regenerative reheating furnace wavelet neural network predictive control furnace temperature control
作者简介 薛美盛(1969-),男,副教授,博士;
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