.png){kind=icon}
.png){kind=icon}
Return
Nonlinear model predictive control based on support vector machine and genetic algorithm
.png){kind=icon}
PRE
AI
DOI：10.1016/j.cjche.2015.10.009.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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. (C) 2015 The Chemical Industry and Engineering Society of China, and Chemical Industry Press. All rights reserved.
Keywords:
Support vector machine
Genetic algorithm
Nonlinear model predictive control
Neural network
Modeling
Journal
IF:
3.7
Papers:
5.2K
Citations:
1.1W