{"title":"直流电机的自适应神经控制","authors":"I. Baruch, R. Garrido, J. Flores, J.C. Martinez","doi":"10.1109/ISIC.2001.971495","DOIUrl":null,"url":null,"abstract":"A recurrent trainable neural network (RTNN) together with a backpropagation trough-time learning algorithm are applied for a real-time identification and adaptive control of a DC-motor drive. The paper proposes to use three RTNNs separately for the parts of the systems identification, the state feedback control and the feedforward control. The applied RTNN model has a minimum number of parameters due to its Jordan canonical structure, which permits to use the generated vector of states directly for a DC-motor feedback control. The experimental results, confirm the applicability of the described identification and control methodology in practice and also confirm the good quality of the RTNN.","PeriodicalId":367430,"journal":{"name":"Proceeding of the 2001 IEEE International Symposium on Intelligent Control (ISIC '01) (Cat. No.01CH37206)","volume":"39 6 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2001-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"10","resultStr":"{\"title\":\"An adaptive neural control of a DC motor\",\"authors\":\"I. Baruch, R. Garrido, J. Flores, J.C. Martinez\",\"doi\":\"10.1109/ISIC.2001.971495\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A recurrent trainable neural network (RTNN) together with a backpropagation trough-time learning algorithm are applied for a real-time identification and adaptive control of a DC-motor drive. The paper proposes to use three RTNNs separately for the parts of the systems identification, the state feedback control and the feedforward control. The applied RTNN model has a minimum number of parameters due to its Jordan canonical structure, which permits to use the generated vector of states directly for a DC-motor feedback control. The experimental results, confirm the applicability of the described identification and control methodology in practice and also confirm the good quality of the RTNN.\",\"PeriodicalId\":367430,\"journal\":{\"name\":\"Proceeding of the 2001 IEEE International Symposium on Intelligent Control (ISIC '01) (Cat. No.01CH37206)\",\"volume\":\"39 6 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2001-09-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"10\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceeding of the 2001 IEEE International Symposium on Intelligent Control (ISIC '01) (Cat. No.01CH37206)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISIC.2001.971495\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceeding of the 2001 IEEE International Symposium on Intelligent Control (ISIC '01) (Cat. No.01CH37206)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISIC.2001.971495","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A recurrent trainable neural network (RTNN) together with a backpropagation trough-time learning algorithm are applied for a real-time identification and adaptive control of a DC-motor drive. The paper proposes to use three RTNNs separately for the parts of the systems identification, the state feedback control and the feedforward control. The applied RTNN model has a minimum number of parameters due to its Jordan canonical structure, which permits to use the generated vector of states directly for a DC-motor feedback control. The experimental results, confirm the applicability of the described identification and control methodology in practice and also confirm the good quality of the RTNN.