Application of neural networks for permanent magnet synchronous motor direct torque control

查看全文 作　　者：Zhang [1,2]Chunmei;Liu [1]Heping;Chen [1]Shujin;Wang [1]Fangjun

机构地区：[1]Information Engineering School, Univ. of Science and Technology Beijing, Beijing 100083, China;[2]Engineering Dept., Coll. of Electron Science and Technology Career, Beijing 100029, China

出　　处：《Journal of Systems Engineering and Electronics》索引2008年第19卷第3期,共7页

基　　金：the National Natural Science Foundation of China (60374032). 摘　　要：Neural networks require a lot of training to understand the model of a plant or a process. Issues such as learning speed, stability, and weight convergence remain as areas of research and comparison of many training algorithms. The application of neural networks to control interior permanent magnet synchronous motor using direct torque control (DTC) is discussed. A neural network is used to emulate the state selector of the DTC. The neural networks used are the back-propagation and radial basis function. To reduce the training patterns and increase the execution speed of the training process, the inputs of switching table are converted to digital signals, i.e., one bit represent the flux error, one bit the torque error, and three bits the region of stator flux. Computer simulations of the motor and neural-network system using the two approaches are presented and compared. Discussions about the back-propagation and radial basis function as the most promising training techniques are presented, giving its advantages and disadvantages. The system using back-propagation and radial basis function networks controller has quick parallel speed and high torque response. 关键词：内部永动磁体电动机内部网络转矩控制径直转矩控制

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Application of neural networks for permanent magnet synchronous motor direct torque control

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