摘要
为了提高板形调控效率和控制精度,基于板形控制矩阵和DE-ELM神经网络,建立了冷轧带钢板形控制机理-智能协同调控模型。首先,根据带钢金属模型和辊系弹性模型建立板形控制的机理仿真模型,构建静态板形控制矩阵;同时利用DE-ELM神经网络形成动态板形控制矩阵,并利用加权方法协调板形控制矩阵的影响度,提高板形控制稳定性和精度。实例表明,机理智能协同调控模型能够更快速获得有效板形控制系数,有助于提高冷轧带钢板形调控效率,使不良板形快速调整至良好状态。
Based on the shape control matrix and the DE-ELM neural network, the shape control mechanism-intelli- gence coordination model of cold rolling strip is established to improve the efficiency of shape control and the control precision. First of all, according to the strip metal deformation model and the roll system elastic model, the mechanism simulation model of shape control is obtained for constructing the static shape control matrix. At the same time, a dy- namic shape control matrix forms through DE-ELM neural network, and the weighted method is used to optimize ma- trix shape control in the actual process to reduce shape errors, improve stability and accuracy of the shape control mod- el. The result of example shows that the model can get the effective shape control coefficients more quickly, improve the shape control efficiency, and adjust rapidly the extremely bad shape to a good state.
出处
《钢铁》
CAS
CSCD
北大核心
2017年第7期52-57,共6页
Iron and Steel
基金
国家自然科学基金资助项目(51305387)
河北省自然科学钢铁联合基金资助项目(E2015203103)
关键词
板形测控
协同调控
影响系数
控制矩阵
冷轧带钢
shape detection and control
cooperative control model
influence coefficient
control matrix
cold rolling strip
