Nonlinear model predictive control based on support vector machine with multi-kernel

被引：0
|
作者：
Bao Zhejing [1 ]
Pi Daoying [1 ]
Sun Youxian [1 ]
机构：
[1] Zhejiang Univ, Inst Adv Proc Control, State Key Lab Ind Control Technol, Hangzhou 310027, Peoples R China
来源：
CHINESE JOURNAL OF CHEMICAL ENGINEERING | 2007年 / 15卷 / 05期
关键词：
nonlinear model predictive control; support vector machine with multi-kernel; nonlinear system identification; kernel function;
D O I：
10.1016/S1004-9541(07)60147-5
中图分类号：
TQ [化学工业];
学科分类号：
0817 ;
摘要：
Multi-kernel-based support vector machine (SVM) model structure of nonlinear systems and its specific identification method is proposed, which is composed of a SVM with linear kernel function followed in series by a SVM with spline kernel function. With the help of this model, nonlinear model predictive control can be transformed to linear model predictive control, and consequently a unified analytical solution of optimal input of multi-step-ahead predictive control is possible to derive. This algorithm does not require online iterative optimization in order to be suitable for real-time control with less calculation. The simulation results of pH neutralization process and CSTR reactor show the effectiveness and advantages of the presented algorithm.
引用
收藏
页码：691 / 697
页数：7
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