GA-BASED PID NEURAL NETWORK CONTROL FOR MAGNETIC BEARING SYSTEMS  

作　　者：LI Guodong ZHANG Qingchun LIANG Yingchun 

机构地区：[1]School of Mechanical and Electrical Engineering,Harbin Institute of Technology, Harbin 150001, China

出　　处：《Chinese Journal of Mechanical Engineering》2007年第2期56-59,共4页中国机械工程学报（英文版）

基　　金：This project is supported by National Natural Science Foundation of China (No. 5880203).

摘　　要：In order to overcome the system non-linearity and uncertainty inherent in magnetic bearing systems, a GA（genetic algnrithm）-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 unknown 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.In order to overcome the system non-linearity and uncertainty inherent in magnetic bearing systems, a GA（genetic algnrithm）-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 unknown 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.

关 键 词：Magnetic bearing Non-linearity PID neural network Genetic algorithm Local minima Robust performance 

分 类 号：TH133.3[机械工程—机械制造及自动化] TP273[自动化与计算机技术—检测技术与自动化装置]