Nonlinearmodel predictive control based on support vectormachine and genetic algorithm

Kai Feng; Jiangang Lu; Jinshui Chen

doi:10.1016/j.cjche.2015.10.009

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Nonlinearmodel predictive control based on support vectormachine and genetic algorithm

第25届中国过程控制会议专栏 | Updated：2026-01-06

- Nonlinearmodel predictive control based on support vectormachine and genetic algorithm
- Nonlinearmodel predictive control based on support vectormachine and genetic algorithm
- 中国化学工程学报（英文版） 2015年23卷第12期页码：2048-2052
- Affiliations：
  
  State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University,Hangzhou,China,310027
- Author bio：
- Funds：
- DOI：10.1016/j.cjche.2015.10.009
  中图分类号：
- 纸质出版：2015
- Accepted：
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Kai Feng, Jiangang Lu, Jinshui Chen. Nonlinearmodel predictive control based on support vectormachine and genetic algorithm[J]. 中国化学工程学报（英文版）, 2015,23(12):2048-2052.

Kai Feng, Jiangang Lu, Jinshui Chen. Nonlinearmodel predictive control based on support vectormachine and genetic algorithm[J]. Chin.J.Chem.Eng., 2015, 23(12): 2048-2052.
Kai Feng, Jiangang Lu, Jinshui Chen. Nonlinearmodel predictive control based on support vectormachine and genetic algorithm[J]. 中国化学工程学报（英文版）, 2015,23(12):2048-2052. DOI： 10.1016/j.cjche.2015.10.009.

Kai Feng, Jiangang Lu, Jinshui Chen. Nonlinearmodel predictive control based on support vectormachine and genetic algorithm[J]. Chin.J.Chem.Eng., 2015, 23(12): 2048-2052. DOI： 10.1016/j.cjche.2015.10.009.

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Abstract

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.

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