New predictive control algorithms based on Least Squares Support Vector Machines

• Published in: Journal of Zhejiang University. A. Science Vol. 6; no. 5; pp. 440 - 446
• Main Author: 刘斌 苏宏业 褚健
• Format: Journal Article
• Language: English
• Published: National Laboratory of Industrial Control Technology, Institute of Advanced Process Control,Zhejiang University, Hangzhou 310027, China 01.05.2005; School of Information Science and Engineering, Wuhan University of Science and Technology, Wuhan 430081, China%National Laboratory of Industrial Control Technology, Institute of Advanced Process Control,Zhejiang University, Hangzhou 310027, China
• Subjects: RBF核心函数; 最小支持向量装置; 线性函数; 自动控制系统; Linear kernel function; Least Squares Support Vector Machines; RBF kernel function; Generalized predictive control
• ISSN: 1673-565X; 1862-1775
• DOI: 10.1631/jzus.2005.A0440

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.