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基于最小二乘支持向量机的MIMO线性参数变化模型辨识及预测控制
引用本文:冯凯,卢建刚,陈金水.基于最小二乘支持向量机的MIMO线性参数变化模型辨识及预测控制[J].化工学报,2015,66(1):197-205.
作者姓名:冯凯  卢建刚  陈金水
作者单位:浙江大学控制科学与工程学系工业控制技术国家重点实验室, 浙江 杭州 310027
基金项目:国家重点基础研究发展计划项目(2012CB720500);国家自然科学基金项目(21076179)。@@@@supported by the National Basic Research Program of China,the National Natural Science Foundation of China
摘    要:将现有的面向单输入单输出系统的基于最小二乘支持向量机的参数变化模型辨识算法(SISO-LSSVM-LPV),推广到多输入多输出系统,实现了面向多输入多输出系统的基于最小二乘支持向量机的参数变化模型辨识算法(MIMO-LSSVM-LPV),进一步结合基于遗传算法的预测控制算法(GA-MPC),提出并实现了MIMO-LSSVM-LPV+GA-MPC的建模控制一体化新架构。仿真结果表明,该辨识算法可逼近复杂非线性MIMO系统,辨识精度高,并且保留了线性回归低计算量的优点,结合了GA的MPC可实现最优控制量的在线实时寻优,并取得了良好控制效果。

关 键 词:非线性系统  最小二乘支持向量机  线性参数变化模型  多输入多输出  模型预测控制  过程控制    参数识别  
收稿时间:2014-10-30
修稿时间:2014-11-07

Identification and model predictive control of LPV models based on LS-SVM for MIMO system
FENG Kai,LU Jiangang,CHEN Jinshui.Identification and model predictive control of LPV models based on LS-SVM for MIMO system[J].Journal of Chemical Industry and Engineering(China),2015,66(1):197-205.
Authors:FENG Kai  LU Jiangang  CHEN Jinshui
Affiliation:State Key Laboratory of Industrial Control Technology, Department of Control Science and Engineering, Zhejiang University, Hangzhou 310027, Zhejiang, China
Abstract:This paper presents a least-square support vector machine based linear parameter-varying model approach for multiple-input multiple-output nonlinear system (MIMO-LSSVM-LPV). The identified model can be used in the model predictive control scheme combined with genetic algorithm (GA-MPC). The new identification and controlling integration scheme is named MIMO-LSSVM-LPV+GA-MPC. Simulation results show that the identification algorithm can approximate complex nonlinearity with high accuracy while keep the advantage of low computational burden of linear regression. GA based MPC can get the real-time optimal control input and achieve good controlling performance.
Keywords:nonlinear system  least-square support vector machine (LSSVM)  linear parameter-varying (LPV) model  multiple-input multiple-output (MIMO)  model predictive control (MPC)  process control  parameter identification
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