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

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

BAO Zhejing; PI Daoying; SUN Youxian

State Key Laboratory of Industrial Control Technology, Institute of Industrial Process Control, Zhejiang University, Hangzhou 310027, China

Received:2006-09-14 Revised:1900-01-01 Online:2007-10-28 Published:2007-10-28
Contact: BAO Zhejing

基于多核支持向量机的非线性模型预测控制

包哲静; 皮道映; 孙优贤

State Key Laboratory of Industrial Control Technology, Institute of Industrial Process Control, Zhejiang University, Hangzhou 310027, China

通讯作者: 包哲静

Abstract

Abstract: 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 trans-formed 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 optimiza-tion 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.

Key words: nonlinear model predictive control, support vector machine with multi-kernel, nonlinear system identification, kernel function

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 trans-formed 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 optimiza-tion 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.

关键词: nonlinear model predictive control;support vector machine with multi-kernel;nonlinear system identification;kernel function

BAO Zhejing, PI Daoying, SUN Youxian. Nonlinear model predictive control based on support vector machine with multi-kernel[J]. , DOI: .

包哲静, 皮道映, 孙优贤. 基于多核支持向量机的非线性模型预测控制[J]. , DOI: .

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