{"title":"The study and simulation of PID control based on RBF neural network","authors":"Yi-fei Chen, Sen Xu, Rui Cao, Tian Zhou","doi":"10.1109/EMEIT.2011.6023826","DOIUrl":null,"url":null,"abstract":"The industrial control system is a complex nonlinear time-varying system, the traditional PID control is limited to linear system, and therefore the control effect is not ideal. In order to improve the control precision, this paper proposes a control method based on RBF neural network and. Firstly discrete models is identification by RBFNN controller and get PID parameters tuning information, then use single neuron controller to set the parameter so as to realize the intelligent control system. The proposed method is verified, the results show that the control method has faster response time, higher control precision compared with the traditional PID control methods; it is a strong adaptability, robustness and anti-interference ability.","PeriodicalId":221663,"journal":{"name":"International Conference on Electronic and Mechanical Engineering and Information Technology","volume":"18 3 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Conference on Electronic and Mechanical Engineering and Information Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/EMEIT.2011.6023826","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
The industrial control system is a complex nonlinear time-varying system, the traditional PID control is limited to linear system, and therefore the control effect is not ideal. In order to improve the control precision, this paper proposes a control method based on RBF neural network and. Firstly discrete models is identification by RBFNN controller and get PID parameters tuning information, then use single neuron controller to set the parameter so as to realize the intelligent control system. The proposed method is verified, the results show that the control method has faster response time, higher control precision compared with the traditional PID control methods; it is a strong adaptability, robustness and anti-interference ability.