In cold rolling process, the flatness actuator efficiency is the basis of the flatness control system. The precision of flatness is determined by the setpoints of flatness actuators. In the presence of modeling uncertainties and unmodeled nonlinearities in rolling process, it is difficult to obtain efficiency factors and setpoints of flatness actuators accurately. Based on the production data, a method to obtain the flatness actuator efficiency by using partial least square (PLS) combined with orthogonal signal correction (OSC) was adopted. Compared with the experiential method and principal component analysis method, the OSC–PLS method shows superior performance in obtaining the flatness actuator efficiency factors at the last stand. Furthermore, kernel partial least square combined with artificial neural network (KPLS–ANN) was proposed to predict the flatness values and optimize the setpoints of flatness actuators. Compared with KPLS or ANN, KPLS–ANN shows the best predictive ability. The root mean square error, mean absolute error and mean absolute percentage error are 0.51 IU, 0.34 IU and 0.09, respectively. After the setpoints of flatness actuators are optimized, KPLS–ANN shows better optimization ability. The result in an average flatness standard deviation is 2.22 IU, while the unoptimized value is 4.10 IU.

在冷轧过程中，平面度执行器效率是平面度控制系统的基础。平面度的精度由平面度执行器的设定值确定。在轧制过程中存在建模不确定性和未建模非线性的情况下，很难准确获得平面度执行器的效率因子和设定点。根据生产数据，采用通过结合偏最小二乘（PLS）和正交信号校正（OSC）来获得平面度执行器效率的方法。与经验方法和主成分分析方法相比，OSC–PLS方法在获得最后的平直执行器效率因子方面表现出优异的性能。此外，提出将核偏最小二乘结合人工神经网络（KPLS–ANN）来预测平面度值并优化平面度执行器的设定点。与KPLS或ANN相比，KPLS–ANN具有最佳的预测能力。均方根误差，平均绝对误差和平均绝对百分比误差分别为0.51 IU，0.34 IU和0.09。优化平面度执行器的设定点后，KPLS–ANN显示出更好的优化能力。平均平坦度标准偏差的结果是2.22 IU，而未优化的值是4.10 IU。优化平面度执行器的设定点后，KPLS–ANN显示出更好的优化能力。平均平坦度标准偏差的结果是2.22 IU，而未优化的值是4.10 IU。优化平面度执行器的设定点后，KPLS–ANN显示出更好的优化能力。平均平坦度标准偏差的结果是2.22 IU，而未优化的值是4.10 IU。