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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。
In order to improve the regulation efficiency and control accuracy, a cold plate strip shape control mechanism - intelligent coordinated control model was established based on the plate shape control matrix and DE-ELM neural network. First of all, based on the strip metal model and the elastic model of the roller system, the mechanism simulation model of the plate-shaped control is established to build the static plate-shaped control matrix. At the same time, the dynamic plate-shaped control matrix is formed by the DE-ELM neural network. Matrix influence, improve shape stability and accuracy control. The example shows that the mechanism of intelligent collaborative control model can obtain effective control parameters of plate shape more quickly, which helps to improve the control efficiency of plate shape of cold rolled strip steel and quickly adjust the bad plate shape to good condition.