| GA-BASED PID NEURAL NETWORK CONTROL FOR MAGNETIC BEARING SYSTEMS |
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| 作者姓名： | LI Guodong ZHANG Qingchun LIANG Yingchun School of Mechanical and Electrical Engineering Harbin Institute of Technology Harbin China |
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| 作者单位： | LI Guodong ZHANG Qingchun LIANG Yingchun School of Mechanical and Electrical Engineering，Harbin Institute of Technology，Harbin 150001，China |
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| 基金项目： | This project is supported by National Natural Science Foundation of China (No. 5880203). |
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| 摘 要： | In order to overcome the system non-linearity and uncertainty inherent in magnetic bear-ing systems,a GA(genetic algorithm)-based PID neural network controller is designed and trained to emulate the operation of a complete system (magnetic bearing,controller,and power amplifiers). The feasibility of using a neural network to control nonlinear magnetic bearing systems with un-known dynamics is demonstrated. The key concept of the control scheme is to use GA to evaluate the candidate solutions (chromosomes),increase the generalization ability of PID neural network and avoid suffering from the local minima problem in network learning due to the use of gradient descent learning method. The simulation results show that the proposed architecture provides well robust performance and better reinforcement learning capability in controlling magnetic bearing systems.
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GA-BASED PID NEURAL NETWORK CONTROL FOR MAGNETIC BEARING SYSTEMS |
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| LI Guodong ZHANG Qingchun LIANG Yingchun School of Mechanical and Electrical Engineering,Harbin Institute of Technology,Harbin ,China.GA-BASED PID NEURAL NETWORK CONTROL FOR MAGNETIC BEARING SYSTEMS[J].Chinese Journal of Mechanical Engineering,2007(2). |
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| Authors: | LI Guodong ZHANG Qingchun LIANG Yingchun School of Mechanical and Electrical Engineering Harbin Institute of Technology Harbin China |
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| Affiliation: | LI Guodong ZHANG Qingchun LIANG Yingchun School of Mechanical and Electrical Engineering,Harbin Institute of Technology,Harbin 150001,China |
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| Abstract: | In order to overcome the system non-linearity and uncertainty inherent in magnetic bear-ing systems,a GA(genetic algorithm)-based PID neural network controller is designed and trained to emulate the operation of a complete system (magnetic bearing,controller,and power amplifiers). The feasibility of using a neural network to control nonlinear magnetic bearing systems with un-known dynamics is demonstrated. The key concept of the control scheme is to use GA to evaluate the candidate solutions (chromosomes),increase the generalization ability of PID neural network and avoid suffering from the local minima problem in network learning due to the use of gradient descent learning method. The simulation results show that the proposed architecture provides well robust performance and better reinforcement learning capability in controlling magnetic bearing systems. |
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| Keywords: | Magnetic bearing Non-linearity PID neural network Genetic algorithm Local minima Robust performance |
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