Modeling of Permanent-magnet Linear Synchronous Motor Using Hybrid Nonlinear Autoregressive Neural Network

摘要：

The modeling of permanent-magnet linear synchronous motor is very important to the control and the static and dynamic characters analysis of the system.In this paper,the model of permanent-magnet linear synchronous motor is presented by using neural networks of the nonlinear autoregressive with exogenous inputs.Based on the same cost function,residual signal analysis is mixed into the networks,and then the networks can identify motors order automatically.First,the nonlinear autoregressive with exogenous inputs model is expanded into the polynomial function,then the condition which true order satisfy is presented by using residual signal analysis.Some shortages of BP (back-propagation algorithm) are considered,so NDEKF ((node-decoupled extend Kalman filter)is applied to train networks.The experiment results show that the hybrid neural networks of the nonlinear autoregressive with exogenous inputs can identify objects (a vertical transport system driven by permanent-magnet linear synchronous motor) order precisely,and the output of networks is very close to the experimental result.In the experiments,the performance of NDEKF is often superior to that of BP,while requiring significantly fewer presentations of training data than BP and less over training time than that of BP.

关键词： neural networks permanent-magnet linear synchronous motor identification hybrid nonlinear autoregressive neural network NDEKF

作者: LV Gang LIU Zhiming FAN Yu LI Guo-guo

作者单位: School of Mechanics& Electric Control Engineering,Beijing Jiaotong University,Beijing,100044,China;C School of Mechanics& Electric Control Engineering,Beijing Jiaotong University,Beijing,100044,China School of Electrical Engineering,Beijing Jiaotong University,Beijing,100044,China

会议类型: 国际会议

会议名称: 9th International Conference on Signal Processing(第九届国际信号处理学术会议)(ICSP08)

会议地点: 北京

会议语种:英文

在线出版日期: 2008-10-26（万方平台首次上网日期，不代表论文的发表时间）