Fitting of Dynamic Characters for Magnetic Bearing Control System by using Deep Neural Network

摘要：

　　Magnetic Bearing Control System(MBCS)has two major aporias, non-linear and strong coupling.Most of the traditional methods are severely weakened by these two problems.This paper presents a noval control algorithm to optimize the performance of Magnetic Bearing Control System .The proposed method estimates the non-linear dynamic characters and decouples system by using a deep neural network(DNN).The inputs of DNN are system PID controller parameters and some other dynamic coefficients, outputs are Magnetic Bearing speed,torque and power output characters.We utilize root-mean-square error(RMSE)as the loss function of DNN since the network is a regression model.Experiments showed that the proposed method could affectively fit and decouple dynamic characters for MBCS and performs competitively against Humans.

关键词： Magnetic bearing Control system deep neural network regression model

作者: LUO Yanyan CHEN Jinping ZHANG Li ZHANG Haining LIU Jun

作者单位: AVIC Qingan group co.,LTD,710077,Xian,China

会议类型: 国际会议

会议名称: the 16th International Symposium on Magnetic Bearings (第十六届国际磁悬浮轴承大会) (ISMB6)

会议地点: 北京

会议语种:英文

页码: 1-4

在线出版日期: 2018-08-13（万方平台首次上网日期，不代表论文的发表时间）