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| Received:April 28, 2004Revised:September 22, 2004 |
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| Temperature prediction control based on least squares support vector machines |
| Bin LIU, Hongye SU, Weihua HUANG, Jian CHU |
| (National Laboratory of Industrial Control Technology, Institute of Advanced Process Control, Zhejiang University, Yuquan Campus,Hangzhou Zhejiang 310027,China; Department of Automation, College of Information Science and Engineering, Wuhan University of Saence and Technology, Wuhan Hubei 430081, China) |
| Abstract: |
| A prediction control algorithm is presented based on least squares support vector machines (LS-SVM) model for a class of complex systems with strong nonlinearity. The nonlinear off-line model of the controlled plant is built by LS-SVM with radial basis function (RBF) kernel. In the process of system running, the off-line model is linearized at each sampling instant, and the generalized prediction control (GPC) algorithm is employed to implement the prediction control for the controlled plant.The obtained algorithm is applied to a boiler temperature control system with complicated nonlinearity and large time delay.The results of the experiment verify the effectiveness and merit of the algorithm. |
| Key words: Predictive control Least squares support vector machines RBF kernel function Generalized prediction control |
