学术论文

      New predictive control algorithms based on Least Squares Support Vector Machines

      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.
      Author: LIU Bin [1] SU Hong-ye [2] CHU Jian [2]
      作者单位: National Laboratory of Industrial Control Technology, Institute of Advanced Process Control,Zhejiang University, Hangzhou 310027, China;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
      刊 名: 浙江大学学报A(英文版) ISTICEISCI
      年,卷(期): 2005, 6(5)
      分类号: TP273
      机标分类号: TP2 O1
      在线出版日期: 2005年6月9日
      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