Research On Flatness Intelligent Control For Cold Strip Mill Based On Improved Least Squares Support Vector Regression

Plate and strip steel, which is the main composition of the steel products and widelyused in automotive, food packaging industry, has significant influence to nationaleconomy. With the progress of science and technology, higher requirement to steel qualitywas made by customers. Flatness is one of the most important quality indexes of strip steel,and flatness controlling technique is the hot topic in the rolling area. In recent years,artificial intelligence has been used widely in industrial process study for its merits inmodeling, optimization and control. This paper choose the cold strip mill flatnessintelligent control based on improved Least Squares Support Vector Regression(LS-SVR)algorithms as research object. On analyzing the merits and defects of the existingintelligent control approach, improved LS-SVR algorithms was studied andcomprehensive research on flatness controlling system was made.First of all, a novel Multi-output Least Squares Support Vector Regression (MLSSVR)approach was proposed to overcome the defects that standard LS-SVR algorithm, whichapplies only to more inputs single output system, can not use on more inputs more outputsindustrial process directly. The MLSSVR algorithm still meets the principle of StructuralRisk Minimization, therefore, keeps good generalization performance. Furthermore, totackle the difficulty in determining the hyper-parameters of MLSSVR, an optimizationmethod based on particle swarm optimization algorithm was adopted. This approach cannot only compute effectively, but also has strong searching ability.Flatness pattern recognition is the key constituent of the flatness control system. Inorder to adapt to the higher demand of flatness controlling, flatness basic patternsexpressed by the linear, quadratic, cubic and quartic Legendre orthogonal polynomial wereproposed. And a novel flatness pattern recognition method based on MLSSVR was putforward. The results of experiment demonstrate that the proposed approach can distinguishthe types and define the magnitudes of the flatness defects effectively with high accuracy,high speed and strong generalization ability.Flatness predictive model is the most important foundation of control system. Inorder to have higher precise flatness predictive model, a MLSSVR flatness predictive model is designed on the basis of measured data in production. Simulation experimentdemonstrates that the MLSSVR flatness predictive model has higher predictive accuracyand strong robustness.Finally, effective matrix--predictive control approach was put forward oncomprehensively analyzing the characteristics of effective matrix control method andpredictive control method and combining the merits of the tow methods. Then, simulationexperiment on testing the performance of the control model was conducted on900HCreversible cold roll. It demonstrates that effective matrix--predictive control approach hasbetter control effect than effective matrix control method, therefore, is an effective flatnesscontrol method.