Taking 1420 mm UCM six-high cold rolling mill as the research object, a non square flatness control system with five input and four output is decoupled into a square subsystem with two input and two output which controls the primary and cubic flatness and a non square subsystem with three input and two output which controls the quadratic and quartic flatness by using the relative gain theory. By decomposing the unstable poles of the generalized inverse matrix of the non square system, the method of the generalized inverse matrix decoupling control the quadratic and quartic flatness is proposed, which solves the unstable problem of decoupling of non-square system. According to the characteristics of intermediate roll shifting, the variable model of roll shifting influence coefficient and the control strategy of minimum roll shifting adjustment and threshold are proposed. The dynamic characteristics of the system are improved and the adjustment of intermediate roll shifting is reduced. In order to overcome the shortcomings of low accuracy and poor generalization ability of shallow neural network, a mechanism-intelligent influence matrix model based on big data and deep neural network is proposed. Simulation calculation and industrial application show that the control system runs stably, the adjustment speed is fast, the control precision is high, the change of intermediate roll shifting is small, and it is suitable for online control.

以1420 mm UCM六辊冷轧机为研究对象，将一个五进四出的非方平直度控制系统解耦为一个两进二出方方子系统，控制一次方和方方平直度，一个非方方平直度控制系统。三输入两输出子系统，利用相对增益理论控制二次和四次平坦度。通过分解非方阵广义逆矩阵的不稳定极点，提出了广义逆矩阵解耦控制二次和四次平坦度的方法，解决了非方阵解耦不稳定的问题。根据中间辊移位的特点，提出了侧倾影响系数的变量模型和最小侧倾调整和阈值的控制策略。提高了系统的动态特性，减少了中间辊移位的调整。为了克服浅层神经网络精度低、泛化能力差的缺点，提出了一种基于大数据和深层神经网络的机制智能影响矩阵模型。仿真计算和工业应用表明，控制系统运行稳定，调整速度快，控制精度高，中间辊位移变化小，适合在线控制。为了克服浅层神经网络精度低、泛化能力差的缺点，提出了一种基于大数据和深层神经网络的机制智能影响矩阵模型。仿真计算和工业应用表明，该控制系统运行稳定，调整速度快，控制精度高，中间辊位移变化小，适合在线控制。为了克服浅层神经网络精度低、泛化能力差的缺点，提出了一种基于大数据和深层神经网络的机制智能影响矩阵模型。仿真计算和工业应用表明，控制系统运行稳定，调整速度快，控制精度高，中间辊位移变化小，适合在线控制。