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基于支持向量机和遗传算法的非线性模型预测控制（英文）

Nonlinear model predictive control based on support vector machine and genetic algorithm

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【Author】 Kai Feng;Jiangang Lu;Jinshui Chen;State Key Laboratory of Industrial Control Technology,College of Control Science and Engineering,Zhejiang University;

【机构】 State Key Laboratory of Industrial Control Technology,College of Control Science and Engineering,Zhejiang University；

【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.更多还原

【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.更多还原

【关键词】 Support vector machine； Genetic algorithm； Nonlinear model predictive control； Neural network； Modeling；
【Key words】 Support vector machine； Genetic algorithm； Nonlinear model predictive control； Neural network； Modeling；

【基金】 Supported by the National Natural Science Foundation of China(21076179);the National Basic Research Program of China(2012CB720500)

【文献出处】 Chinese Journal of Chemical Engineering ,中国化学工程学报(英文版) , 编辑部邮箱 ,2015年12期

【分类号】TP18
【被引频次】35
【下载频次】289

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