首页|Nonlinear model predictive control based on support vector machine and genetic algorithm
Nonlinear model predictive control based on support vector machine and genetic algorithm
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
万方数据
维普
This paper presents a nonlinear model predictive control (NMPC) approach based on support vector machine (SVM) and genetic algorithm (GA) for multiple-input multiple-output (MIMO) nonlinear systems.Individual SVM is used to approximate each output of the controlled plant.Then the model is used in MPC control scheme to predict the outputs of the controlled plant.The optimal control sequence is calculated using GA with elite preserve strategy.Simulation results of a typical MIMO nonlinear system show that this method has a good ability of set points tracking and disturbance rejection.
Support vector machineGenetic algorithmNonlinear model predictive controlNeural networkModeling
Kai Feng、Jiangang Lu、Jinshui Chen
展开 >
State Key Laboratory of Industrial Control Technology,College of Control Science and Engineering,Zhejiang University,Hangzhou 310027,China
Supported by the National Natural Science Foundation of ChinaNational Basic Research Program of China
NETL
NSTL
万方数据
维普
