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		Zhejing BAO1,Youxian SUN.[en_title][J].Control Theory and Technology,2008,6(3):305~310.[Copy]

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ZhejingBAO1,YouxianSUN
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Received:May 18, 2006Revised:July 04, 2007

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Support vector machine-based multi-model predictive control

Zhejing BAO1, Youxian SUN

(College of Electrical Engineering, Zhejiang University, Hangzhou Zhejiang 310027, China; State Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou Zhejiang 310027, China)

Abstract:

In this paper, a support vector machine-based multi-model predictive control is proposed, in which SVM classification combines well with SVM regression. At first, each working environment is modeled by SVM regression and the support vector machine network-based model predictive control (SVMN-MPC) algorithm corresponding to each environment is developed, and then a multi-class SVM model is established to recognize multiple operating conditions. As for control, the current environment is identified by the multi-class SVM model and then the corresponding SVMN-MPC controller is activated at each sampling instant. The proposed modeling, switching and controller design is demonstrated in simulation results.

Key words: Multi-model predictive control Support vector machine network Multi-class support vector machine Multi-model switching