Temperature prediction control based on least squares support vector machines

被引：6
|
作者：
Bin Liu
Hongye Su
Weihua Huang
Jian Chu
机构：
[1] Zhejiang University,National Laboratory of Industrial Control Technology, Institute of Advanced Process Control
[2] Yuquan Campus,Department of Automation, College of Information Science and Engineering
[3] Wuhan University of Science and Technology,undefined
来源：
Journal of Control Theory and Applications | 2004年 / 2卷 / 4期
关键词：
Predictive control; Least squares support vector machines; RBF kernel function; Generalized prediction control;
D O I：
10.1007/s11768-004-0041-7
中图分类号：
学科分类号：
摘要：
A prediction control algorithm is presented based on least squares support vector machines (LS-SVM) model for a class of complex systems with strong nonlinearity. The nonlinear off-line model of the controlled plant is built by LS-SVM with radial basis function (RBF) kernel. In the process of system running, the off-line model is linearized at each sampling instant, and the generalized prediction control (GPC) algorithm is employed to implement the prediction control for the controlled plant. The obtained algorithm is applied to a boiler temperature control system with complicated nonlinearity and large time delay. The results of the experiment verify the effectiveness and merit of the algorithm.
引用
收藏
页码：365 / 370
页数：5
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