A NONLINEAR MODEL PREDICTIVE CONTROL BASED ON LEAST SQUARES SUPPORT VECTOR MACHINES NARX MODEL

摘要：

In the domain of industry process control, the model identification and predictive control of nonlinear systems are always difficult problems.To solve the problems, an identification method based on least squares support vector machines for function approximation is utilized to identify a nonlinear autoregressive external input (NARX) model.The NARX model is then used to construct a novel nonlinear model predictive controller.In deriving the control law, a quasi-Newton algorithm is selected to implement the nonlinear model predictive control (NMPC) algorithm.The simulation result illustrates the validity and feasibility of the nonlinear MPC algorithm.

关键词： Least squares support vector machines (LS-SVM) NARX model identification Nonlinear model predictive control Quasi-Newton algorithm

作者: YUN-TAO SHI DE-HUI SUN QING WANG SI-CHENG NIAN LI-ZHI XIANG

作者单位: North China University of Technology, Beijing, 100041 Institute of Automation, Chinese Academy of Science, Beijing, 100080

会议类型: 国际会议

会议名称: 2007 International Conference on Machine Learning and Cybernetics(IEEE第六届机器学习与控制论国际会议)

会议地点: 香港

会议语种:英文

页码: 721-725

在线出版日期: 2007-08-19（万方平台首次上网日期，不代表论文的发表时间）