Flatness intelligent control via improved least squares support vector regression algorithm

被引：0

作者：

张秀玲 ^{[1
,2
]}

张少宇 ^{[1
]}

赵文保 ^{[1
]}

徐腾 ^{[1
]}

机构：

[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

来源：

JournalofCentralSouthUniversity | 2013年 / 20卷 / 03期

基金：

中国国家自然科学基金;

关键词：

least squares support vector regression; multi-output least squares support vector regression; flatness; effective matrix; predictive control;

D O I：

暂无

中图分类号：

TP273.5 []; O212.1 [一般数理统计];

学科分类号：

020208 ; 070103 ; 0714 ; 080201 ; 0835 ;

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.

引用

页码：688 / 695

页数：8

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