| New predictive control algorithms based on Least Squares Support Vector Machines |
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| 作者姓名： | 刘斌 苏宏业 褚健 |
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| 作者单位： | National Laboratory of Industrial Control Technology，Institute of Advanced Process Control，Zhejiang UniversityHangzhou 310027，China，School of Information Science and Engineering，Wuhan University of Science and Technology，Wuhan 430081，China，School of Information Science and Engineering，Wuhan University of Science and Technology，Wuhan 430081，China，School of Information Science and Engineering，Wuhan University of Science and Technology，Wuhan 430081，China |
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| 基金项目： | 国家杰出青年科学基金，高等学校优秀青年教师教学科研奖励计划 |
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| 摘 要： | INTRODUCTION Model predictive control (MPC), based on pre-dictive model and receding horizon optimization, has become an attractive feedback control strategy, be-cause it has found successful applications, especially in the process industry. For this kind of control strategy, the predictive model is a crucial component because the essence of MPC is to optimize the fore-cast of process behavior (Rawlings, 2000), and the forecast is accomplished with the predictive model. If the controll…
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| 关 键 词： | 最小支持向量装置 线性函数 RBF核心函数 自动控制系统 |
| 收稿时间： | 17 March 2009 |
New predictive control algorithms based on Least Squares Support Vector Machines |
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| Liu Bin,Su Hong-ye,Chu Jian.New predictive control algorithms based on Least Squares Support Vector Machines[J].Journal of Zhejiang University Science,2005,6(5):440-446. |
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| Authors: | Liu Bin Su Hong-ye Chu Jian |
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| Institution: | (1) National Laboratory of Industrial Control Technology, Institute of Advanced Process Control, Zhejiang University, 310027 Hangzhou, China;(2) School of Information Science and Engineering, Wuhan University of Science and Technology, 430081 Wuhan, China |
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| Abstract: | Used for industrial process with different degree of nonlinearity, the two predictive control algorithms presented in this paper are based on Least Squares Support Vector Machines (LS-SVM) model. For the weakly nonlinear system, the system model is built by using LS-SVM with linear kernel function, and then the obtained linear LS-SVM model is transformed into linear input-output relation of the controlled system. However, for the strongly nonlinear system, the off-line model of the controlled system is built by using LS-SVM with Radial Basis Function (RBF) kernel. The obtained nonlinear LS-SVM model is linearized at each sampling instant of system running, after which the on-line linear input-output model of the system is built. Based on the obtained linear input-output model, the Generalized Predictive Control (GPC) algorithm is employed to implement predictive control for the controlled plant in both algorithms. The simulation results after the presented algorithms were implemented in two different industrial processes model; respectively revealed the effectiveness and merit of both algorithms. |
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| Keywords: | Least Squares Support Vector Machines Linear kernel function RBF kernel function Generalized predictive control |
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