Nonlinearmodel predictive control based on support vectormachine and genetic algorithm

doi:10.1016/j.cjche.2015.10.009

Chin.J.Chem.Eng. ›› 2015, Vol. 23 ›› Issue (12): 2048-2052.DOI: 10.1016/j.cjche.2015.10.009

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Nonlinearmodel predictive control based on support vectormachine and genetic algorithm

Kai Feng, Jiangang Lu, Jinshui Chen

State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China

Received:2015-06-01 Revised:2015-09-07 Online:2016-01-19 Published:2015-12-28
Contact: Jiangang Lu
Supported by:
Supported by the National Natural Science Foundation of China (21076179) and the National Basic Research Program of China (2012CB720500).

Nonlinearmodel predictive control based on support vectormachine and genetic algorithm

Kai Feng, Jiangang Lu, Jinshui Chen

State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China

通讯作者: Jiangang Lu
基金资助:
Supported by the National Natural Science Foundation of China (21076179) and the National Basic Research Program of China (2012CB720500).

Abstract

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.

Key words: Support vector machine, Genetic algorithm, Nonlinear model predictive control, Neural network, Modeling

摘要： 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.

关键词: Support vector machine, Genetic algorithm, Nonlinear model predictive control, Neural network, Modeling

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]. Chinese Journal of Chemical Engineering, 2015, 23(12): 2048-2052.

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Nonlinearmodel predictive control based on support vectormachine and genetic algorithm

Nonlinearmodel predictive control based on support vectormachine and genetic algorithm

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