SHAN Xiu-ying
,
LIU Hong-min
,
JIA Chun-yu
,
SUN Jian-liang
钢铁研究学报(英文版)
Using the effective matrix methods of flatness and profile control synthetically, the flatness and profile integration control scheme for tandem cold mills is built in order to increase flatness and profile control precision of tandem cold mills. Corresponding control strategies are adopted for various control objectives of different stands and the coordination control strategies of various stands are given, which makes the on-line flatness control cooperate with on-line profile control and implements the parallel control of different stands. According to the measured flatness and profile data of some 1550 mm tandem cold mills, the control scheme is verified and the result indicates that the scheme has high flatness and profile control precision with steady and reliable control process. A new way and method is supplied for researching shape control of tandem cold mills.
关键词:
flatness
,
profile
,
shape
,
effective matrix
,
tandem cold mill
ZHANG Xiu-ling
,
ZHANG Shao-yu
,
TAN Guang-zhong
,
ZHAO Wen-bao
钢铁研究学报(英文版)
To adapt to the new requirement of the developing flatness control theory and technology, cubic patterns were introduced on the basis of the traditional linear, quadratic and quartic flatness basic patterns. Linear, quadratic, cubic and quartic Legendre orthogonal polynomials were adopted to express the flatness basic patterns. In order to overcome the defects live in the existent recognition methods based on fuzzy, neural network and support vector regression (SVR) theory, a novel flatness pattern recognition method based on least squares support vector regression (LS-SVR) was proposed. On this basis, for the purpose of determining the hyper-parameters of LS-SVR effectively and enhancing the recognition accuracy and generalization performance of the model, particle swarm optimization algorithm with leave-one-out (LOO) error as fitness function was adopted. To overcome the disadvantage of high computational complexity of naive cross-validation algorithm, a novel fast cross-validation algorithm was introduced to calculate the LOO error of LS-SVR. Results of experiments on flatness data calculated by theory and a 900HC cold-rolling mill practically measured flatness signals demonstrate that the proposed approach can distinguish the types and define the magnitudes of the flatness defects effectively with high accuracy, high speed and strong generalization ability.
关键词:
flatness
,
pattern recognition
,
least squares support vector regression
,
cross-validation
