Flatness intelligent control via improved least squares support vector regression algorithm  

作　　者：张秀玲 张少宇 赵文保 徐腾 

机构地区：[1]Key Laboratory of Industrial Computer Control Engineering of Hebei Province (Yanshan University) [2]National Engineering Research Centre for Equipment and Technology of Cold Strip Rolling

出　　处：《Journal of Central South University》2013年第3期688-695,共8页中南大学学报（英文版）

基　　金：Project(50675186) supported by the National Natural Science Foundation of China

摘　　要：To overcome the disadvantage that the standard least squares support vector regression(LS-SVR) algorithm is not suitable to multiple-input multiple-output(MIMO) system modelling directly,an improved LS-SVR algorithm which was defined as multi-output least squares support vector regression(MLSSVR) was put forward by adding samples' absolute errors in objective function and applied to flatness intelligent control.To solve the poor-precision problem of the control scheme based on effective matrix in flatness control,the predictive control was introduced into the control system and the effective matrix-predictive flatness control method was proposed by combining the merits of the two methods.Simulation experiment was conducted on 900HC reversible cold roll.The performance of effective matrix method and the effective matrix-predictive control method were compared,and the results demonstrate the validity of the effective matrix-predictive control method.To overcome the disadvantage that the standard least squares support vector regression （LS-SVR） algorithm is not suitable to multiple-input multiple-output （MIMO） system modelling directly, an improved LS-SVR algorithm which was defined as multi-output least squares support vector regression （MLSSVR） was put forward by adding samples＇ absolute errors in objective function and applied to flatness intelligent control. To solve the poor-precision problem of the control scheme based on effective matrix in flatness control, the predictive control was introduced into the control system and the effective matrix-predictive flatness control method was proposed by combining the merits of the two methods. Simulation experiment was conducted on 900HC reversible cold roll. The performance of effective matrix method and the effective matrix-predictive control method were compared, and the results demonstrate the validity of the effective matrix-predictive control method.

关 键 词：least squares support vector regression multi-output least squares support vector regression FLATNESS effective matrix predictive control 

分 类 号：TP273.5[自动化与计算机技术—检测技术与自动化装置] O212.1[自动化与计算机技术—控制科学与工程]