Nonlinear model predictive control based on support vector machine and genetic algorithm |
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Authors: | Kai Feng Jiangang Lu Jinshui Chen |
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Affiliation: | State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China |
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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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Keywords: | Support vector machine Genetic algorithm Nonlinear model predictive control Neural network Modeling |
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