Supervisory Predictive Control Based on Least Square Support Vector Machine and Improved Particle Swarm Optimization

摘要：

　　Least square support vector machine is a kind of thought to solve structural risk minimization method,which is used for system identification,nonlinear control,and fault diagnosis,and has important research value.Based on the identification function of least square support vector machine,according to the identified parameters,which are used in supervisory predictive control algorithm,and for function optimization problems,particle swarm optimization algorithm is used to solve the dynamic setpoint optimization problems.Simulation results show that least square support vector machine algorithm learns fast,has good nonlinear modeling and generalization ability,and the supervisory predictive control algorithm based on least square support vector machine and the particle swarm optimization has better control performance.

关键词： support vector machine least square support vector machine model identification particle swarm optimization supervisory predictive control

作者: LI Suzhen LIU Xiangjie YUAN Gang

作者单位: Department of Control and Computer Engineering,North China Electric Power University,Beijing 102206, ZhongXing Hydraulic Parts Co.Ltd,Sany Heavy Industry,loudi 417009,China

会议类型: 国际会议

会议名称: The 33th Chinese Control Conference第33届中国控制会议

会议地点: 南京

会议语种:英文

页码: 1955-1960

在线出版日期: 2014-07-28（万方平台首次上网日期，不代表论文的发表时间）