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一种基于支持向量机的内模控制方法
引用本文:王定成,方廷健. 一种基于支持向量机的内模控制方法[J]. 控制理论与应用, 2004, 21(1): 85-88
作者姓名:王定成  方廷健
作者单位:中国农业大学,精细农业研究中心,北京,100083;中国科学院,合肥智能机械研究所,安徽,合肥,230031;中国科学院,合肥智能机械研究所,安徽,合肥,230031
摘    要:在基于数据的基础上,采用SVM回归理论建立系统的正向模型和设计逆模控制器.首先简要介绍了SVMR的原理,然后将其应用于内模控制问题,并建立了SVMR模型.其次,在控制过程可逆的条件下设计了SVMR控制器.最后将该控制方法应用于一可逆非线性系统和具未知干扰的温室环境控制问题,仿真结果表明该方法与神经网络IMC相比,具有较简单的模型和较好的控制性能.

关 键 词:支持向量机(SVM)  支持向量机回归(SVMR)  内模控制(IMC)  非线性
文章编号:1000-8152(2004)01-0085-04
收稿时间:2002-03-18
修稿时间:2003-04-18

Internal model control approach based on support vector machines
WANG Ding-cheng,FANG Ting-jian. Internal model control approach based on support vector machines[J]. Control Theory & Applications, 2004, 21(1): 85-88
Authors:WANG Ding-cheng  FANG Ting-jian
Affiliation:The Research Center of Precision Agriculture, China Agricultural University, Beijing 100083, China; The Institute of Intelligent Machinery, Chinese Academy of Sciences, Anhui Hefei 230031, China
Abstract:The system process was modeled and an inverse model controller using support vector machine regression (SVMR) was designed. First, the SVMR principle was briefly introduced. Second, the SVMR was applied to the internal model control (IMC) problem, and the SVMR internal model was developed. Third, an SVMR controller for internal model control problem was proposed under the inverse condition of control process. Finally, the control algorithm was applied to the reversible nonlinear system and greenhouse environment with unknown disturbance, and compared with neural networks IMC using simulation, and the results showed that the SVMR IMC has a simplified model and good control performance.
Keywords:support vector machines (SVM)   SVMR   IMC   non-linear
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