| 基于2次核SVM的单步非线性模型预测控制 |
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| 引用本文： | 钟伟民, ,何国龙, ,皮道映, ,孙优贤.基于2次核SVM的单步非线性模型预测控制[J].中国化学工程学报,2005,13(3):373-379. |
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| 作者姓名： | 钟伟民 何国龙 皮道映 孙优贤 |
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| 作者单位： | [1]NationalLaboratoryofIndustrialControlTechnology,InstituteofModernControlEngineering,ZhejiangUniversity,Hangzhou310027,China [2]DepartmentofMathematics,ZhejiangNormalUniversity,Jinhua321004,China |
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| 基金项目： | 国家重点基础研究发展计划(973计划) |
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| 摘 要： | A support vector machine (SVM) with quadratic polynomial kernel function based nonlinear model one-step-ahead predictive controller is presented. The SVM based predictive model is established with black-box identification method. By solving a cubic equation in the feature space, an explicit predictive control law is obtained through the predictive control mechanism. The effect of controller is demonstrated on a recognized benchmark problem and on the control of continuous-stirred tank reactor (CSTR). Simulation results show that SVM with quadratic polynomial kernel function based predictive controller can be well applied to nonlinear systems, with good performance in following reference trajectory as well as in disturbance-rejection.
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| 关 键 词： | SVM 二次方程式 多项式 非线性模型 预测模型 运算法则 控制论 |
| 修稿时间： | |
SVM with Quadratic Polynomial Kernel Function Based Nonlinear Model One-step-ahead Predictive Control |
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| ZHONG Weimin,HE Guolong,PI Daoying,SUN Youxian.SVM with Quadratic Polynomial Kernel Function Based Nonlinear Model One-step-ahead Predictive Control[J].Chinese Journal of Chemical Engineering,2005,13(3):373-379. |
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| Authors: | ZHONG Weimin HE Guolong PI Daoying SUN Youxian |
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| Affiliation: | National Laboratory of Industrial Control Technology, Institute of Modern Control Engineering, Zhejiang University, Hangzhou 310027;Chinab Department of Mathematics, Zhejiang Normal University, Jinhua 321004, China |
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| Abstract: | A support vector machine (SVM) with quadratic polynomial kernel function based nonlinear model one-step-ahead predictive controller is presented. The SVM based predictive model is established with black-box identification method. By solving a cubic equation in the feature space, an explicit predictive control law is obtained through the predictive control mechanism. The effect of controller is demonstrated on a recognized benchmark problem and on the control of continuous-stirred tank reactor (CSTR). Simulation results show that SVM with quadratic polynomial kernel function based predictive controller can be well applied to nonlinear systems, with good performance in following reference trajectory as well as in disturbance-rejection. |
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| Keywords: | nonlinear model predictive control support vector machine nonlinear system identification kernel function |
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