摘要
针对钢坯加热炉的大滞后、非线性、不确定性等特点,提出采用小波神经网络预测控制策略对加热炉炉温进行控制,研究炉温的小波神经网络预测模型、小波神经网络优化控制器,以及反馈校正的设计与实现。结合生产实际,以现场采集的炉温数据进行了大量的仿真研究。结果表明,该控制策略是可行的、有效的。
With regards to the characteristics of large time delays, nonlinear and uncertainty, the wavelet neural network predictive control strategy was proposed to control the furnace-temperature of the reheating furnace. The design of the furnace-temperature predictive model and optimization controller based on wavelet neural network, as well as the feedback correct were discussed. The simulation results based on the actual data of the production processes on site demonstrate the feasibility and effectiveness of the control strategy.
出处
《中南大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2009年第S1期48-52,共5页
Journal of Central South University:Science and Technology
基金
山西省自然科学基金资助项目(2006011033)
关键词
钢坯加热炉
炉温控制
小波神经网络预测控制策略
slab reheating furnace
furnace-temperature control
wavelet neural network predictive control strategy
