Flatness intelligent control via improved least squares support vector regression algorithm
来源期刊：中南大学学报(英文版)2013年第3期
论文作者：ZHANG Xiu-ling(张秀玲) ZHANG Shao-yu(张少宇) ZHAO Wen-bao(赵文保) XU Teng(徐腾)
文章页码：688 - 695
Key words：least squares support vector regression; multi-output least squares support vector regression; flatness; effective matrix; predictive control
Abstract: 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.
ZHANG Xiu-ling(张秀玲)1,2, ZHANG Shao-yu(张少宇)1, ZHAO Wen-bao(赵文保)1, XU Teng(徐腾)1
(1. Key Laboratory of Industrial Computer Control Engineering of Hebei Province
(Yanshan University), Qinhuangdao 066004, China;
2. National Engineering Research Centre for Equipment and Technology of Cold Strip Rolling,
Qinhuangdao 066004, China)
Abstract: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.
Key words:least squares support vector regression; multi-output least squares support vector regression; flatness; effective matrix; predictive control
